3_sarsa.ipynb

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    "# Understanding SARSA: A Complete Guide\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "- [What is SARSA?](#what-is-sarsa)\n",
    "- [Where and How SARSA is Used](#where-and-how-sarsa-is-used)\n",
    "- [Mathematical Foundation of SARSA](#mathematical-foundation-of-sarsa)\n",
    "  - [Complex Original Version](#complex-original-version)\n",
    "  - [Simplified Version](#simplified-version)\n",
    "- [Step-by-Step Explanation of SARSA](#step-by-step-explanation-of-sarsa)\n",
    "- [Key Components of SARSA](#key-components-of-sarsa)\n",
    "  - [Q-Table](#q-table)\n",
    "  - [Exploration vs. Exploitation](#exploration-vs-exploitation)\n",
    "  - [Learning Rate (α)](#learning-rate-α)\n",
    "  - [Discount Factor (γ)](#discount-factor-γ)\n",
    "- [SARSA vs. Q-Learning](#sarsa-vs-q-learning)\n",
    "- [Practical Example: Grid World](#practical-example-grid-world)\n",
    "  - [Setting up the Environment](#setting-up-the-environment)\n",
    "  - [Creating a Simple Environment](#creating-a-simple-environment)\n",
    "  - [Implementing the SARSA Algorithm](#implementing-the-sarsa-algorithm)\n",
    "  - [Exploration vs. Exploitation Strategy](#exploration-vs-exploitation-strategy)\n",
    "  - [Running the SARSA Algorithm](#running-the-sarsa-algorithm)\n",
    "  - [Visualizing the Learning Process](#visualizing-the-learning-process)\n",
    "  - [Analyzing Q-Values and Optimal Policy](#analyzing-q-values-and-optimal-policy)\n",
    "  - [Testing with Different Hyperparameters (Optional)](#testing-with-different-hyperparameters-optional)\n",
    "- [Applying SARSA to Different Environments (Cliff Walking)](#applying-sarsa-to-different-environments-cliff-walking)\n",
    "- [Common Challenges and Solutions](#common-challenges-and-solutions)\n",
    "- [SARSA vs. Other Reinforcement Learning Algorithms](#sarsa-vs-other-reinforcement-learning-algorithms)\n",
    "  - [Advantages of SARSA](#advantages-of-sarsa)\n",
    "  - [Limitations of SARSA](#limitations-of-sarsa)\n",
    "  - [Related Algorithms](#related-algorithms)\n",
    "- [Conclusion](#conclusion)\n",
    "\n",
    "## What is SARSA?\n",
    "\n",
    "SARSA (State-Action-Reward-State-Action) is a reinforcement learning algorithm used to learn a Markov decision process policy. It is a model-free, value-based, **on-policy** learning algorithm. Unlike Q-learning which is off-policy, SARSA learns the value of the policy being followed.\n",
    "\n",
    "The name SARSA reflects the sequence of information needed for a single update: the current state (S), the action taken (A), the reward received (R), the next state (S'), and the *next action* taken (A') according to the current policy in the next state.\n",
    "\n",
    "## Where and How SARSA is Used\n",
    "\n",
    "SARSA is used in similar scenarios as Q-learning, but its on-policy nature makes it particularly suitable for situations where evaluating or improving the current policy being executed is important, or where safety during learning is a concern. Applications include:\n",
    "\n",
    "1.  **Robotics**: Learning navigation or control tasks where following a consistent (and potentially safer) policy during learning is preferred.\n",
    "2.  **Control Systems**: Optimizing controllers where the actions taken directly influence the system's state and subsequent learning (e.g., HVAC control).\n",
    "3.  **Game Playing**: Training agents in environments where the actions of other agents (or the environment's stochasticity) depend on the agent's current policy.\n",
    "4.  **Any situation where learning stability and understanding the value of the *current* behavior is crucial.**\n",
    "\n",
    "SARSA works well in environments where:\n",
    "- The environment has discrete states and actions.\n",
    "- An on-policy approach is desired (learning based on actions actually taken).\n",
    "- The environment is fully observable.\n",
    "\n",
    "## Mathematical Foundation of SARSA\n",
    "\n",
    "### Complex Original Version\n",
    "\n",
    "The SARSA algorithm updates Q-values using the following update rule:\n",
    "\n",
    "$$Q(s_t, a_t) \\leftarrow Q(s_t, a_t) + \\alpha \\left[r_t + \\gamma Q(s_{t+1}, a_{t+1}) - Q(s_t, a_t)\\right]$$\n",
    "\n",
    "Where:\n",
    "- $Q(s_t, a_t)$ is the Q-value for state $s_t$ and action $a_t$.\n",
    "- $\\alpha$ is the learning rate (0 < $\\alpha$ ≤ 1).\n",
    "- $r_t$ is the reward received after taking action $a_t$ in state $s_t$.\n",
    "- $\\gamma$ is the discount factor (0 ≤ $\\gamma$ ≤ 1) for future rewards.\n",
    "- $s_{t+1}$ is the next state observed after taking action $a_t$.\n",
    "- $a_{t+1}$ is the **next action** chosen in state $s_{t+1}$ using the **current policy** (e.g., epsilon-greedy based on current Q-values).\n",
    "- The term in brackets $[r_t + \\gamma Q(s_{t+1}, a_{t+1}) - Q(s_t, a_t)]$ is the temporal difference (TD) error, based on the action actually chosen for the next step.\n",
    "\n",
    "### Simplified Version\n",
    "\n",
    "In simpler terms, the SARSA update can be understood as:\n",
    "\n",
    "$$\n",
    "Q_{\\text{new}} = Q_{\\text{old}} + \\alpha \\left[ R + \\gamma Q_{\\text{next}} - Q_{\\text{old}} \\right]\n",
    "$$\n",
    "\n",
    "Where $Q_{\\text{next}}$ is the Q-value of the *specific state-action pair* $(s', a')$ that will be taken next according to the policy.\n",
    "\n",
    "Or even more simply:\n",
    "\n",
    "$$\n",
    "Q_{\\text{new}} = Q_{\\text{old}} + \\alpha \\left[ \\text{Target} - Q_{\\text{old}} \\right]\n",
    "$$\n",
    "\n",
    "Where \"Target\" is the reward plus the discounted value of the *next state-action pair chosen by the policy*.\n",
    "\n",
    "## Step-by-Step Explanation of SARSA\n",
    "\n",
    "1.  **Initialize the Q-table**: Create a table with rows for each state and columns for each action, initially filled with zeros or small random values.\n",
    "2.  **Start an episode**: Begin in an initial state $s$.\n",
    "3.  **Choose an action**: In state $s$, select an action $a$ using an exploration/exploitation policy (like epsilon-greedy) based on current Q-values.\n",
    "4.  **Loop for each step of episode**:\n",
    "    a.  **Take action**: Execute action $a$, observe the reward $r$ and the new state $s'$.\n",
    "    b.  **Choose next action**: In the new state $s'$, select the *next action* $a'$ using the same policy (e.g., epsilon-greedy) based on current Q-values for state $s'$.\n",
    "    c.  **Update Q-value**: Apply the SARSA update formula using $s, a, r, s', a'$.\n",
    "    d.  **Update state and action**: Set $s \\leftarrow s'$ and $a \\leftarrow a'$.\n",
    "    e.  **Check for termination**: If $s$ is a terminal state, end the episode.\n",
    "5.  **Repeat for multiple episodes**: Run many episodes to allow the agent to refine its Q-values based on the policy it follows.\n",
    "\n",
    "## Key Components of SARSA\n",
    "\n",
    "### Q-Table\n",
    "A Q-table is a lookup table where:\n",
    "- Rows represent states in the environment.\n",
    "- Columns represent possible actions.\n",
    "- Each cell contains a Q-value representing the expected future reward for taking that action in that state *and following the current policy thereafter*.\n",
    "\n",
    "For example, in a simple grid world:\n",
    "\n",
    "| State | Up | Down | Left | Right |\n",
    "|-------|---|------|------|-------|\n",
    "| (0,0) | 0.0 | 0.0 | 0.0 | 0.0 |\n",
    "| (0,1) | 0.0 | 0.0 | 0.0 | 0.0 |\n",
    "| ... | ... | ... | ... | ... |\n",
    "\n",
    "### Exploration vs. Exploitation\n",
    "The balance between exploration (trying new actions) and exploitation (using known good actions) is crucial. SARSA, like Q-learning, commonly uses the **epsilon-greedy** strategy:\n",
    "- With probability $\\epsilon$, choose a random action (explore).\n",
    "- With probability $1-\\epsilon$, choose the action with the highest Q-value (exploit).\n",
    "- $\\epsilon$ typically decreases over time (epsilon decay).\n",
    "\n",
    "### Learning Rate (α)\n",
    "- Controls how much new information overrides old information in the Q-value update.\n",
    "- High $\\alpha$ (near 1): Learns quickly, potentially unstable.\n",
    "- Low $\\alpha$ (near 0): Learns slowly, more stable convergence.\n",
    "- Typical values: 0.01 to 0.5.\n",
    "\n",
    "### Discount Factor (γ)\n",
    "- Determines the importance of future rewards compared to immediate rewards.\n",
    "- $\\gamma = 0$: Only considers immediate rewards (myopic).\n",
    "- $\\gamma = 1$: Values future rewards equally to immediate rewards (far-sighted, potentially non-convergent if rewards don't stop).\n",
    "- Typical values: 0.9 to 0.99.\n",
    "\n",
    "## SARSA vs. Q-Learning\n",
    "\n",
    "The primary difference lies in how the target value for the Q-update is calculated:\n",
    "\n",
    "-   **Q-Learning (Off-Policy)**: Uses the maximum possible Q-value in the next state ($max_{a'} Q(s', a')$). It learns the optimal policy regardless of the policy being followed during exploration.\n",
    "    $$Q(s, a) \\leftarrow Q(s, a) + \\alpha [r + \\gamma \\max_{a'} Q(s', a') - Q(s, a)]$$\n",
    "-   **SARSA (On-Policy)**: Uses the Q-value of the *actual next action* chosen by the current policy ($Q(s', a')$). It learns the value of the policy it is currently executing (including its exploration steps).\n",
    "    $$Q(s, a) \\leftarrow Q(s, a) + \\alpha [r + \\gamma Q(s', a') - Q(s, a)]$$\n",
    "\n",
    "This makes SARSA generally more conservative, especially in environments with risks (like cliffs), as it considers the potential negative outcomes of exploratory moves. Q-learning aims directly for the optimal path, sometimes learning riskier policies if they promise higher rewards eventually.\n",
    "\n",
    "## Practical Example: Grid World\n",
    "\n",
    "In the following sections, we will apply SARSA to the same grid world environment used in the Q-Learning example:\n",
    "- A 4×4 grid.\n",
    "- Terminal states at (0,0) with reward 1 and (3,3) with reward 10.\n",
    "- All other states have reward 0.\n",
    "- The agent can move up, down, left, or right.\n",
    "- The goal is to learn a policy to reach the highest reward state.\n",
    "\n",
    "We will implement the algorithm, run the training, and visualize the learned Q-values and policy, comparing them implicitly to what Q-Learning might find."
   ]
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   "source": [
    "# Setting up the Environment\n",
    "Import necessary libraries including NumPy for numerical operations and Matplotlib for visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "# Import necessary libraries\n",
    "import numpy as np  # For numerical operations\n",
    "import matplotlib.pyplot as plt  # For visualizations\n",
    "\n",
    "# Import type hints\n",
    "from typing import List, Tuple, Dict, Optional\n",
    "\n",
    "# set seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Enable inline plotting for Jupyter Notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Simple Environment\n",
    "\n",
    "To create a simple environment for the SARSA algorithm, we will define a 4x4 GridWorld. The GridWorld will have the following properties:\n",
    "\n",
    "- 4 rows and 4 columns\n",
    "- Possible actions: 'up', 'down', 'left', 'right'\n",
    "- Specific terminal states with rewards.\n",
    "- Cliff states (in the Cliff Walking example later)."
   ]
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# Define the GridWorld environment creator function\n",
    "def create_gridworld(\n",
    "    rows: int,\n",
    "    cols: int,\n",
    "    terminal_states: List[Tuple[int, int]],\n",
    "    rewards: Dict[Tuple[int, int], int]\n",
    ") -> Tuple[np.ndarray, List[Tuple[int, int]], List[str]]:\n",
    "    \"\"\"\n",
    "    Create a simple GridWorld environment.\n",
    "\n",
    "    Parameters:\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    - terminal_states (List[Tuple[int, int]]): List of terminal states as (row, col) tuples.\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping (row, col) to reward values.\n",
    "\n",
    "    Returns:\n",
    "    - grid (np.ndarray): A 2D array representing the grid with rewards (for reference, not used by agent).\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states in the grid.\n",
    "    - action_space (List[str]): List of possible actions ('up', 'down', 'left', 'right').\n",
    "    \"\"\"\n",
    "    # Initialize the grid with zeros (for visualization/reference)\n",
    "    grid = np.zeros((rows, cols))\n",
    "\n",
    "    # Assign rewards to specified states\n",
    "    for (row, col), reward in rewards.items():\n",
    "        grid[row, col] = reward\n",
    "\n",
    "    # Define the state space as all possible (row, col) pairs\n",
    "    state_space = [\n",
    "        (row, col)\n",
    "        for row in range(rows)\n",
    "        for col in range(cols)\n",
    "    ]\n",
    "\n",
    "    # Define the action space as the four possible movements\n",
    "    action_space = ['up', 'down', 'left', 'right']\n",
    "\n",
    "    return grid, state_space, action_space"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need the state transition function, which takes the current state and action as input and returns the next state. Think of this as the agent moving around the grid based on the action it takes. The environment is deterministic in this case."
   ]
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   "cell_type": "code",
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   "source": [
    "# Define state transition function\n",
    "def state_transition(state: Tuple[int, int], action: str, rows: int, cols: int) -> Tuple[int, int]:\n",
    "    \"\"\"\n",
    "    Compute the next state given the current state and action. Handles boundaries.\n",
    "\n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - action (str): Action to take ('up', 'down', 'left', 'right').\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple[int, int]: The resulting state (row, col) after taking the action.\n",
    "    \"\"\"\n",
    "    # Unpack the current state into row and column\n",
    "    row, col = state\n",
    "    next_row, next_col = row, col\n",
    "\n",
    "    # Update the row or column based on the action, ensuring boundaries are respected\n",
    "    if action == 'up' and row > 0:  # Move up if not in the topmost row\n",
    "        next_row -= 1\n",
    "    elif action == 'down' and row < rows - 1:  # Move down if not in the bottommost row\n",
    "        next_row += 1\n",
    "    elif action == 'left' and col > 0:  # Move left if not in the leftmost column\n",
    "        next_col -= 1\n",
    "    elif action == 'right' and col < cols - 1:  # Move right if not in the rightmost column\n",
    "        next_col += 1\n",
    "    # If action would move off grid, stay in the same state (row, col remain unchanged)\n",
    "\n",
    "    # Return the new state as a tuple\n",
    "    return (next_row, next_col)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our agent can interact with the environment, we need to define the reward function. This function will return the reward for arriving in a given state, which will be used to update the Q-values during training."
   ]
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   "cell_type": "code",
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   "source": [
    "# Define reward function\n",
    "def get_reward(state: Tuple[int, int], rewards: Dict[Tuple[int, int], int]) -> int:\n",
    "    \"\"\"\n",
    "    Get the reward for a given state.\n",
    "\n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping state (row, col) to reward values.\n",
    "\n",
    "    Returns:\n",
    "    - int: The reward for the given state. Returns 0 if the state is not in the rewards dictionary.\n",
    "    \"\"\"\n",
    "    # Use the rewards dictionary to fetch the reward for the given state.\n",
    "    # If the state is not found, return a default reward of 0.\n",
    "    return rewards.get(state, 0)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined the GridWorld environment and the necessary helper functions, let's test them with a simple example. We will create a 4x4 grid with two terminal states at (0, 0) and (3, 3) having rewards of 1 and 10, respectively. We will then test the state transition and reward functions by moving from the state (2, 2) upwards."
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridWorld (rewards view):\n",
      "[[ 1.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.]\n",
      " [ 0.  0.  0. 10.]]\n",
      "Current State: (2, 2)\n",
      "Action Taken: up\n",
      "Next State: (1, 2)\n",
      "Reward at Next State: 0\n"
     ]
    }
   ],
   "source": [
    "# Example usage of the GridWorld environment\n",
    "\n",
    "# Define the grid dimensions (4x4), terminal states, and rewards\n",
    "rows, cols = 4, 4  # Number of rows and columns in the grid\n",
    "terminal_states = [(0, 0), (3, 3)]  # Terminal states\n",
    "rewards = {(0, 0): 1, (3, 3): 10}  # Rewards for terminal states (other states have reward 0)\n",
    "\n",
    "# Create the GridWorld environment\n",
    "grid, state_space, action_space = create_gridworld(rows, cols, terminal_states, rewards)\n",
    "\n",
    "# Test the state transition and reward functions\n",
    "current_state = (2, 2)  # Starting state\n",
    "action = 'up'  # Action to take\n",
    "next_state = state_transition(current_state, action, rows, cols)  # Compute the next state\n",
    "reward = get_reward(next_state, rewards)  # Get the reward for the next state\n",
    "\n",
    "# Print the results\n",
    "print(\"GridWorld (rewards view):\")  # Display the grid with rewards\n",
    "print(grid)\n",
    "print(f\"Current State: {current_state}\")  # Display the current state\n",
    "print(f\"Action Taken: {action}\")  # Display the action taken\n",
    "print(f\"Next State: {next_state}\")  # Display the resulting next state\n",
    "print(f\"Reward at Next State: {reward}\")  # Display the reward for the next state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the GridWorld environment is created with the specified dimensions, terminal states, and rewards. We selected a starting state (2, 2) and an action ('up'). The next state is computed as (1, 2), and the reward for arriving in state (1, 2) is 0 because it's not a specified reward state.\n",
    "\n",
    "# Implementing the SARSA Algorithm\n",
    "\n",
    "We have successfully implemented the GridWorld environment. Now we implement the SARSA algorithm. First, initialize the Q-table, mapping state-action pairs to Q-values (expected cumulative reward)."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Q-table\n",
    "def initialize_q_table(state_space: List[Tuple[int, int]], action_space: List[str]) -> Dict[Tuple[Tuple[int, int], str], float]:\n",
    "    \"\"\"\n",
    "    Initialize the Q-table with zeros for all state-action pairs.\n",
    "    Uses a single dictionary with (state, action) tuples as keys.\n",
    "\n",
    "    Parameters:\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states.\n",
    "    - action_space (List[str]): List of all possible actions.\n",
    "\n",
    "    Returns:\n",
    "    - q_table (Dict[Tuple[Tuple[int, int], str], float]): A dictionary mapping (state, action) pairs to Q-values, initialized to 0.0.\n",
    "    \"\"\"\n",
    "    q_table: Dict[Tuple[Tuple[int, int], str], float] = {}\n",
    "    for state in state_space:\n",
    "        for action in action_space:\n",
    "            # Initialize Q-value for the (state, action) pair to 0.0\n",
    "            q_table[(state, action)] = 0.0\n",
    "    return q_table\n",
    "\n",
    "# --- Alternative Q-Table Structure (Nested Dictionary) ---\n",
    "# for easier comparison and reuse. The run_sarsa_episode will adapt.\n",
    "def initialize_q_table_nested(state_space: List[Tuple[int, int]], action_space: List[str]) -> Dict[Tuple[int, int], Dict[str, float]]:\n",
    "    \"\"\"\n",
    "    Initialize the Q-table with zeros using a nested dictionary structure.\n",
    "\n",
    "    Parameters:\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states.\n",
    "    - action_space (List[str]): List of all possible actions.\n",
    "\n",
    "    Returns:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): A nested dictionary where q_table[state][action] gives the Q-value.\n",
    "    \"\"\"\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]] = {}\n",
    "    for state in state_space:\n",
    "        # For terminal states, Q-values should remain 0 as no actions can be taken from them.\n",
    "        # However, initializing all allows easier lookup during updates before termination check.\n",
    "        q_table[state] = {action: 0.0 for action in action_space}\n",
    "    return q_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the epsilon-greedy policy for action selection. If a random value is less than epsilon, explore (choose random action); otherwise, exploit (choose the best-known action)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose action using epsilon-greedy policy (using nested Q-table structure)\n",
    "def epsilon_greedy_policy(\n",
    "    state: Tuple[int, int],\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]],\n",
    "    action_space: List[str],\n",
    "    epsilon: float\n",
    ") -> str:\n",
    "    \"\"\"\n",
    "    Choose an action using the epsilon-greedy policy from a nested Q-table.\n",
    "\n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Nested Q-table mapping state -> action -> Q-value.\n",
    "    - action_space (List[str]): List of possible actions.\n",
    "    - epsilon (float): Exploration rate (0 <= epsilon <= 1).\n",
    "\n",
    "    Returns:\n",
    "    - str: The chosen action.\n",
    "    \"\"\"\n",
    "    # Check if state is valid and exists in Q-table\n",
    "    if state not in q_table:\n",
    "        # Handle unseen states if necessary, e.g., return a random action\n",
    "        return np.random.choice(action_space)\n",
    "\n",
    "    # With probability epsilon, choose a random action (exploration)\n",
    "    if np.random.rand() < epsilon:\n",
    "        return np.random.choice(action_space)\n",
    "    # Otherwise, choose the action with the highest Q-value for the current state (exploitation)\n",
    "    else:\n",
    "        # Ensure the state exists and has actions before finding the max\n",
    "        if q_table[state]:\n",
    "            return max(q_table[state], key=q_table[state].get)\n",
    "        else:\n",
    "            # If state has no actions (e.g., might happen with sparse initialization), choose randomly\n",
    "            return np.random.choice(action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we take action $a$ in state $s$, observe reward $r$ and next state $s'$, we choose the *next action* $a'$ using our policy in state $s'$. Then, we update the Q-value for $(s, a)$ using the SARSA update rule:\n",
    "\n",
    "$$\n",
    "Q(s, a) \\leftarrow Q(s, a) + \\alpha \\left[ r + \\gamma Q(s', a') - Q(s, a) \\right]\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update Q-value using SARSA rule (using nested Q-table structure)\n",
    "def update_sarsa_value(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]],\n",
    "    state: Tuple[int, int],\n",
    "    action: str,\n",
    "    reward: int,\n",
    "    next_state: Tuple[int, int],\n",
    "    next_action: str, # The action chosen by the policy in the next state\n",
    "    alpha: float,\n",
    "    gamma: float\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Update the Q-value using the SARSA update rule with a nested Q-table.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Nested Q-table.\n",
    "    - state (Tuple[int, int]): Current state (s).\n",
    "    - action (str): Action taken (a).\n",
    "    - reward (int): Reward received (r).\n",
    "    - next_state (Tuple[int, int]): Next state observed (s').\n",
    "    - next_action (str): Action chosen in the next state by the policy (a').\n",
    "    - alpha (float): Learning rate.\n",
    "    - gamma (float): Discount factor.\n",
    "\n",
    "    Returns:\n",
    "    - None: Updates the Q-table in place.\n",
    "    \"\"\"\n",
    "    # Check if states and actions exist in the Q-table to avoid KeyErrors\n",
    "    if state not in q_table or action not in q_table[state]:\n",
    "        # Optionally initialize if state/action pair is new (shouldn't happen with full init)\n",
    "        # Or log a warning/error\n",
    "        return\n",
    "    if next_state not in q_table or next_action not in q_table[next_state]:\n",
    "         # If next state is terminal or action invalid, Q(s', a') is often treated as 0\n",
    "         # Or handle unseen states appropriately if initialization wasn't exhaustive\n",
    "         q_next = 0.0\n",
    "    else:\n",
    "        # Get the Q-value for the next state and the *actual* next action chosen\n",
    "        q_next: float = q_table[next_state][next_action]\n",
    "\n",
    "    # Calculate the TD target: R + gamma * Q(s', a')\n",
    "    td_target: float = reward + gamma * q_next\n",
    "\n",
    "    # Calculate the TD error: TD Target - Q(s, a)\n",
    "    td_error: float = td_target - q_table[state][action]\n",
    "\n",
    "    # Update the Q-value for the current state-action pair using the SARSA formula\n",
    "    q_table[state][action] += alpha * td_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have defined the environment and the core SARSA update logic. Now, we combine these into a function that runs a single episode of SARSA. This involves stepping through the environment, choosing actions, and updating Q-values until a terminal state is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run a single episode of SARSA\n",
    "def run_sarsa_episode(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]],\n",
    "    state_space: List[Tuple[int, int]],\n",
    "    action_space: List[str],\n",
    "    rewards: Dict[Tuple[int, int], int],\n",
    "    terminal_states: List[Tuple[int, int]],\n",
    "    rows: int,\n",
    "    cols: int,\n",
    "    alpha: float,\n",
    "    gamma: float,\n",
    "    epsilon: float,\n",
    "    max_steps: int\n",
    ") -> Tuple[int, int]:\n",
    "    \"\"\"\n",
    "    Run a single episode of SARSA.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Nested Q-table.\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states.\n",
    "    - action_space (List[str]): List of possible actions.\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping states to rewards.\n",
    "    - terminal_states (List[Tuple[int, int]]): List of terminal states.\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    - alpha (float): Learning rate.\n",
    "    - gamma (float): Discount factor.\n",
    "    - epsilon (float): Exploration rate.\n",
    "    - max_steps (int): Maximum steps per episode.\n",
    "\n",
    "    Returns:\n",
    "    - total_reward (int): Total reward accumulated during the episode.\n",
    "    - steps (int): Number of steps taken in the episode.\n",
    "    \"\"\"\n",
    "    # Start from a random non-terminal state if needed, or a fixed start state\n",
    "    # Here, starting from a random state for broader initial exploration\n",
    "    state: Tuple[int, int] = state_space[np.random.choice(len(state_space))]\n",
    "    while state in terminal_states: # Ensure starting state is not terminal\n",
    "        state = state_space[np.random.choice(len(state_space))]\n",
    "\n",
    "    # Choose the first action 'a' using the policy in the starting state 's'\n",
    "    action: str = epsilon_greedy_policy(state, q_table, action_space, epsilon)\n",
    "\n",
    "    total_reward: int = 0  # Initialize total reward for the episode\n",
    "    steps: int = 0  # Initialize step counter\n",
    "\n",
    "    # Loop for a maximum number of steps or until a terminal state is reached\n",
    "    for _ in range(max_steps):\n",
    "        # Take action 'a', observe next state 's'' and reward 'r'\n",
    "        next_state: Tuple[int, int] = state_transition(state, action, rows, cols)\n",
    "        reward: int = get_reward(next_state, rewards)\n",
    "        total_reward += reward # Accumulate reward\n",
    "\n",
    "        # Choose the *next* action 'a'' from the *next* state 's'' using the policy\n",
    "        next_action: str = epsilon_greedy_policy(next_state, q_table, action_space, epsilon)\n",
    "\n",
    "        # Update the Q-value for the state-action pair (s, a) using SARSA rule\n",
    "        # Note: Q-value is updated based on s, a, r, s', a'\n",
    "        update_sarsa_value(q_table, state, action, reward, next_state, next_action, alpha, gamma)\n",
    "\n",
    "        # Update the current state and action for the next iteration\n",
    "        state = next_state\n",
    "        action = next_action\n",
    "\n",
    "        steps += 1\n",
    "\n",
    "        # Check if the agent has reached a terminal state\n",
    "        if state in terminal_states:\n",
    "            break # End the episode if terminal state is reached\n",
    "\n",
    "    # Return the total reward and number of steps for this episode\n",
    "    return total_reward, steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration vs. Exploitation Strategy\n",
    "\n",
    "To properly balance exploration and exploitation, we use the epsilon-greedy policy defined earlier and implement dynamic epsilon adjustment. Epsilon starts high (more exploration) and gradually decays, encouraging more exploitation as the agent learns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dynamic epsilon adjustment function (same as in Q-Learning reference)\n",
    "def adjust_epsilon(\n",
    "    initial_epsilon: float,\n",
    "    min_epsilon: float,\n",
    "    decay_rate: float,\n",
    "    episode: int\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Dynamically adjust epsilon over time using exponential decay.\n",
    "\n",
    "    Parameters:\n",
    "    - initial_epsilon (float): Initial exploration rate.\n",
    "    - min_epsilon (float): Minimum exploration rate.\n",
    "    - decay_rate (float): Rate at which epsilon decays.\n",
    "    - episode (int): Current episode number.\n",
    "\n",
    "    Returns:\n",
    "    - float: Adjusted exploration rate for the current episode.\n",
    "    \"\"\"\n",
    "    # Compute the decayed epsilon value, ensuring it doesn't go below the minimum epsilon\n",
    "    return max(min_epsilon, initial_epsilon * np.exp(-decay_rate * episode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's track and plot the epsilon decay over the planned episodes to visualize the exploration strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example usage of dynamic epsilon adjustment and plotting the decay\n",
    "\n",
    "# Define epsilon parameters\n",
    "initial_epsilon: float = 1.0  # Start with full exploration\n",
    "min_epsilon: float = 0.1  # Minimum exploration rate\n",
    "decay_rate: float = 0.01  # Decay rate for epsilon\n",
    "episodes: int = 500  # Total number of episodes for training\n",
    "\n",
    "# Track epsilon values over episodes\n",
    "epsilon_values: List[float] = []\n",
    "for episode in range(episodes):\n",
    "    # Adjust epsilon for the current episode\n",
    "    current_epsilon = adjust_epsilon(initial_epsilon, min_epsilon, decay_rate, episode)\n",
    "    epsilon_values.append(current_epsilon)\n",
    "    \n",
    "\n",
    "# Plot epsilon decay over episodes\n",
    "plt.figure(figsize=(20, 3)) # Adjusted figure size slightly\n",
    "plt.plot(epsilon_values)\n",
    "plt.xlabel('Episode')  # Label for the x-axis\n",
    "plt.ylabel('Epsilon')  # Label for the y-axis\n",
    "plt.title('Epsilon Decay Over Episodes')  # Title of the plot\n",
    "plt.grid(True) # Add grid for better readability\n",
    "plt.show()  # Display the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows epsilon starting at 1.0 (pure exploration) and decaying exponentially towards the minimum value of 0.1 (mostly exploitation with 10% exploration). This gradual shift allows the agent to discover the environment initially and then refine its policy based on what it has learned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the SARSA Algorithm\n",
    "\n",
    "Now we run the SARSA algorithm over multiple episodes in the GridWorld environment, using the dynamic epsilon adjustment. We will track the total reward and episode length for each episode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to execute SARSA over multiple episodes and track performance\n",
    "def run_sarsa(\n",
    "    state_space: List[Tuple[int, int]],\n",
    "    action_space: List[str],\n",
    "    rewards: Dict[Tuple[int, int], int],\n",
    "    terminal_states: List[Tuple[int, int]],\n",
    "    rows: int,\n",
    "    cols: int,\n",
    "    alpha: float,\n",
    "    gamma: float,\n",
    "    initial_epsilon: float,\n",
    "    min_epsilon: float,\n",
    "    decay_rate: float,\n",
    "    episodes: int,\n",
    "    max_steps: int\n",
    ") -> Tuple[Dict[Tuple[int, int], Dict[str, float]], List[int], List[int]]:\n",
    "    \"\"\"\n",
    "    Execute the SARSA algorithm over multiple episodes with epsilon decay.\n",
    "\n",
    "    Parameters:\n",
    "    - state_space, action_space, rewards, terminal_states, rows, cols: Environment parameters.\n",
    "    - alpha, gamma: SARSA hyperparameters.\n",
    "    - initial_epsilon, min_epsilon, decay_rate: Epsilon parameters.\n",
    "    - episodes: Number of episodes to run.\n",
    "    - max_steps: Maximum steps per episode.\n",
    "\n",
    "    Returns:\n",
    "    - q_table: The learned Q-table (nested dictionary).\n",
    "    - rewards_per_episode: List of total rewards per episode.\n",
    "    - episode_lengths: List of episode lengths.\n",
    "    \"\"\"\n",
    "    # Initialize the Q-table (using nested structure for compatibility with helpers)\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]] = initialize_q_table_nested(state_space, action_space)\n",
    "\n",
    "    # Initialize lists to store metrics\n",
    "    rewards_per_episode: List[int] = []\n",
    "    episode_lengths: List[int] = []\n",
    "\n",
    "    # Loop through each episode\n",
    "    for episode in range(episodes):\n",
    "        # Adjust epsilon for the current episode\n",
    "        epsilon: float = adjust_epsilon(initial_epsilon, min_epsilon, decay_rate, episode)\n",
    "\n",
    "        # Run a single episode of SARSA\n",
    "        total_reward, steps = run_sarsa_episode(\n",
    "            q_table, state_space, action_space, rewards, terminal_states,\n",
    "            rows, cols, alpha, gamma, epsilon, max_steps\n",
    "        )\n",
    "\n",
    "        # Append metrics for the current episode\n",
    "        rewards_per_episode.append(total_reward)\n",
    "        episode_lengths.append(steps)\n",
    "\n",
    "    # Return the learned Q-table and metrics\n",
    "    return q_table, rewards_per_episode, episode_lengths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the hyperparameters and run the SARSA training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SARSA training completed.\n"
     ]
    }
   ],
   "source": [
    "# Set hyperparameters for SARSA\n",
    "alpha: float = 0.1          # Learning rate\n",
    "gamma: float = 0.9          # Discount factor\n",
    "initial_epsilon: float = 1.0  # Initial exploration rate\n",
    "min_epsilon: float = 0.1      # Minimum exploration rate\n",
    "decay_rate: float = 0.01     # Epsilon decay rate\n",
    "episodes: int = 500         # Number of episodes\n",
    "max_steps: int = 100        # Maximum steps per episode\n",
    "\n",
    "# Define the GridWorld environment parameters again for clarity\n",
    "rows, cols = 4, 4\n",
    "terminal_states = [(0, 0), (3, 3)]\n",
    "rewards = {(0, 0): 1, (3, 3): 10}\n",
    "grid, state_space, action_space = create_gridworld(rows, cols, terminal_states, rewards)\n",
    "\n",
    "# Execute the SARSA algorithm\n",
    "sarsa_q_table, sarsa_rewards_per_episode, sarsa_episode_lengths = run_sarsa(\n",
    "    state_space, action_space, rewards, terminal_states, rows, cols, alpha, gamma,\n",
    "    initial_epsilon, min_epsilon, decay_rate, episodes, max_steps\n",
    ")\n",
    "\n",
    "print(\"SARSA training completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Learning Process\n",
    "\n",
    "Let's visualize the training progress by plotting the total rewards per episode and the length of each episode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust figure size for better visibility\n",
    "plt.figure(figsize=(20, 3))\n",
    "\n",
    "# Plot total rewards per episode\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(sarsa_rewards_per_episode)\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Total Reward')\n",
    "plt.title('SARSA: Cumulative Rewards Over Episodes')\n",
    "plt.grid(True)\n",
    "\n",
    "# Plot episode lengths per episode\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(sarsa_episode_lengths)\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Episode Length')\n",
    "plt.title('SARSA: Episode Lengths Over Episodes')\n",
    "plt.grid(True)\n",
    "\n",
    "# Adjust layout and display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of SARSA Learning Curves**\n",
    "\n",
    "**Left Graph: Cumulative Rewards Over Episodes**  \n",
    "- The rewards fluctuate significantly in the early episodes due to **high exploration (epsilon-greedy policy)**.  \n",
    "- Over time, the agent **increasingly finds the goal state (reward 10)**, but the pattern remains somewhat erratic, indicating continued exploration.  \n",
    "- **Unlike deterministic Q-learning, SARSA maintains some variance in rewards** because it learns based on the actions taken rather than the best possible action at each step.  \n",
    "- The agent does **eventually stabilize towards achieving higher rewards**, but occasional low-reward episodes suggest intermittent suboptimal exploration.  \n",
    "\n",
    "**Right Graph: Episode Lengths Over Episodes**  \n",
    "- The **initial episodes show high episode lengths**, indicating inefficient and exploratory paths.  \n",
    "- **As learning progresses, episode lengths decrease significantly**, suggesting the agent is finding shorter, more optimal paths.  \n",
    "- **Some fluctuations remain even in later episodes**, likely due to the on-policy nature of SARSA, where exploration continues to influence decision-making.  \n",
    "- The overall **downward trend confirms successful learning**, as the agent consistently reaches the goal faster.  \n",
    "\n",
    "**Overall Interpretation**  \n",
    "- **The SARSA agent successfully learns to navigate the environment**, reducing episode lengths and improving cumulative rewards.  \n",
    "- **Compared to Q-learning, SARSA exhibits more variability** in its learning curves due to its policy-dependent updates.  \n",
    "- The final policy is **not purely greedy**, as SARSA balances exploration and exploitation more naturally.  \n",
    "- The overall **pattern follows expected reinforcement learning behavior**: initial instability, gradual improvement, and eventual convergence to near-optimal performance.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Q-Values and Optimal Policy\n",
    "\n",
    "Now, we visualize the learned Q-values and the resulting policy derived from the SARSA algorithm. We use heatmaps for Q-values per action and arrows on the grid for the policy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to visualize the Q-value heatmap\n",
    "def plot_q_values_heatmap(q_table: Dict[Tuple[int, int], Dict[str, float]], rows: int, cols: int, action_space: List[str], fig: plt.Figure, axes: np.ndarray) -> None:\n",
    "    \"\"\"\n",
    "    Visualize Q-values as heatmaps for each action on provided axes.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: Nested dictionary mapping states to actions and their Q-values.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - action_space: List of possible actions (e.g., ['up', 'down', 'left', 'right']).\n",
    "    - fig: Matplotlib figure object.\n",
    "    - axes: Array of Matplotlib axes for plotting heatmaps.\n",
    "    \"\"\"\n",
    "    for i, action in enumerate(action_space):\n",
    "        # Initialize a grid with -inf for states not in the Q-table\n",
    "        q_values = np.full((rows, cols), -np.inf)\n",
    "        for (row, col), actions in q_table.items():\n",
    "            if action in actions:\n",
    "                q_values[row, col] = actions[action]  # Assign Q-value for the action\n",
    "\n",
    "        # Plot the heatmap for the current action\n",
    "        ax = axes[i]\n",
    "        cax = ax.matshow(q_values, cmap='viridis')  # Use 'viridis' colormap\n",
    "        fig.colorbar(cax, ax=ax)  # Add a colorbar to the heatmap\n",
    "        ax.set_title(f\"SARSA Q-values: {action}\")  # Title for the heatmap\n",
    "\n",
    "        # Add gridlines for better visualization\n",
    "        ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "        ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "        ax.grid(which='minor', color='w', linestyle='-', linewidth=1)\n",
    "\n",
    "        # Remove tick labels for a cleaner look\n",
    "        ax.set_xticks(np.arange(cols))\n",
    "        ax.set_yticks(np.arange(rows))\n",
    "        ax.tick_params(axis='both', which='both', length=0)\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "\n",
    "\n",
    "# Function to visualize the learned policy\n",
    "def plot_policy_grid(q_table: Dict[Tuple[int, int], Dict[str, float]], rows: int, cols: int, terminal_states: List[Tuple[int, int]], ax: plt.Axes) -> None:\n",
    "    \"\"\"\n",
    "    Visualize the learned policy as arrows on the grid on a provided axis.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: Nested dictionary mapping states to actions and their Q-values.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - terminal_states: List of terminal states in the grid.\n",
    "    - ax: Matplotlib axis for plotting the policy grid.\n",
    "    \"\"\"\n",
    "    # Initialize a grid to store the policy symbols\n",
    "    policy_grid = np.empty((rows, cols), dtype=str)\n",
    "    action_symbols = {'up': '↑', 'down': '↓', 'left': '←', 'right': '→', '': ''}\n",
    "\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state = (r, c)\n",
    "            if state in terminal_states:\n",
    "                # Mark terminal states with 'T'\n",
    "                policy_grid[r, c] = 'T'\n",
    "                continue\n",
    "            if state in q_table and q_table[state]:\n",
    "                # Find the action(s) with the highest Q-value\n",
    "                max_q = -np.inf\n",
    "                best_actions = []\n",
    "                for action, q_val in q_table[state].items():\n",
    "                    if q_val > max_q:\n",
    "                        max_q = q_val\n",
    "                        best_actions = [action]\n",
    "                    elif q_val == max_q:\n",
    "                        best_actions.append(action)\n",
    "\n",
    "                # Choose the first action in case of ties\n",
    "                if best_actions:\n",
    "                    best_action = best_actions[0]\n",
    "                    policy_grid[r, c] = action_symbols[best_action]\n",
    "                else:\n",
    "                    # Mark states with no valid actions\n",
    "                    policy_grid[r, c] = '.'\n",
    "            else:\n",
    "                # Mark states not visited or without Q-values\n",
    "                policy_grid[r, c] = '.'\n",
    "\n",
    "    # Plot the policy grid\n",
    "    ax.matshow(np.zeros((rows, cols)), cmap='Greys', alpha=0.1)  # Background grid\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            # Add the policy symbol to each cell\n",
    "            ax.text(c, r, policy_grid[r, c], ha='center', va='center', fontsize=14, color='black' if policy_grid[r, c] != 'T' else 'red')\n",
    "\n",
    "    # Add gridlines and title\n",
    "    ax.set_title(\"SARSA Learned Policy\")\n",
    "    ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "    ax.grid(which='minor', color='black', linestyle='-', linewidth=1)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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WrPRzGjlyZKP7nXXWWel+DV9P3bE77bRTo6w59thj0+v56KOP6vfJlMLJjfY7x4899lixm9JhxdyI53hJ+UFhnOO25xy3vfh7OJ+sMrK7heIUUE899VT41re+la5YvPDCC9OVjQMGDEhXrC4qXrEaryKNUw5F8UrIOAVRc1eyxumR4pQ/cfRWnGpuzpw56WrHOJ1oQ/HqyjgKK04d9pe//CVdDRkfN66D9MorrzS6b5xuKd6n7oreKI4Ki6NBm7qydVEff/xxffubU/f9eN8liSOU4zpOcVq0OvHq4jgaLX6vTrxauE68QjdOl1W3BtPkyZNDa7j11lvTSLh4lWx8/Lptp512StO8xvWsojhNWLxydWkjqeOVzw2nfGot8b0Qp3+qE6euilf/xtF5cWQgbas6lyt4i2bMmJH+Pddt8Srp5rzwwgvpyvM4ndnhhx+epn1rbj28uJZaXIMtTgUXrwyP/5aGDRsW3n777TY7J8iC5siC/LPg3nvvTa9lyy23rN8Xf+b77rtvo/vFkSpxBE4c+d1w7cy4bmbMgziNaBS/F//t17U3/vzjzzyO1oifvcX3axRHeceRK4tOgRnPe8ORO3VTV8aRLRQnN6g8sqVpsqXl/Yw4e0icJeoHP/hB+lnVtSM+ZxwFGNvR3PIZtC/ZwdLIiKbJiOwZEfsRS1uf++GHH05rtMdR4IuO3m7OoYce2mjJvvga4+uxzFrbkBsAUEuxuwDxj/24ZvKHH36Ypn6LRav4h/V3vvOdNBVQnfiHfixIxumIpk6dWr/FaYfuvvvuNJXcouI0QvEP2VjcigXN2MFo+OF2Q7HDED+0ju2IazrHDzHi88VpkBtO4RSnZYrTkMbOSF0b4pRTW221VV7TR8XOw9I6DnXfr1ujKbY7FmLrtroprurWiottati+uB51nLK5Trz/6NGj03TMsbMRO11xiqq6x24N8YOf+++/Pz12wy12MKLYEYriH/exbXEa8TjlUuwExuPaS1y7adE1vuvOVVsU12msphW2KBalGm6xkN2cWMCOH07G9RXj9F+jRo1q9LuloW222Sb9roj/huJUaPF3U3wfxymOaVuyYHGyIH/xQ5/4oWNT//4XvV9T+5dffvm0lmDDD4/iB0pxesu4FmJ8T/Tr1y998LjRRhvVT2UepzmvK2Q3tPrqqze6XVf4ju8ripMbVCbZsjjZUlg7ovi35KJtufbaa9OyOq31mimM7CAfMmJxMiK7utezJHV9jPh5VEO9evVqdIFsQ/oT7UtuAECtZf/7lQLED5pjZyNu8Q/QuKZPvEIzrk8TxVGW0bHHHpu2Rd1+++3pmIY22GCD+j9wR44cmdbyiVddxnWIBg0a1GQ7YuEsXuUbt+WWWy785je/SUWyWPiK6joRsaPTlDhqK35g3pw4ojRewRo/DGmuQPfPf/4znY94VXEUOwexHXViW+IVtPH4+LpiB+qXv/xlmD17dlpvKK4d11Bck+nJJ58Mxx9/fOp8xFGu8Yrb2EFp6eiDeEVpQ/Fx4jk74YQTmrx/XYcndppi4TFekXzfffel7frrr08dwIavMV+LFq6bax+VK/5bqutUxqvkn3nmmbSObz4F7Pg7oO7DBNqHLPg/sqC44vsjfqAYR/3EDyDritrxa7z96quvhvfff7/JYndzoztaa21CIBvZ8n9kS8vVvZ641mp8rU2Jrx8oLzLi/8iI7BqOXm9N+hMAQDEodreyzTffPH2dOXNm/R9zcWqnHXbYYbFpf6Jzzjkn/eG/aAdjUeeff376Y/zcc88N48aNy6sd8Y/eunZMmzYt/aF+5JFH1nc4Gv6B/b//+7+pnaeddlqzjxmvzo2PETtP++2332Lfj6OL44fo3/72t+v/aI5/tDe8b8MrP+MUUbGNcVqkeMVxPFcNp42KV33G78XpseLVxYuOTFia+FwfffRRo31xCti6c1Lny1/+cpg3b159h25JYucpnoe4xfMWf6ax8Hj66acvdqVrPu2LYhsbTiPb3NROsWgZz1HDIvlrr72Wvq655pqZnpvsqkMubYUcX6j4nosd/Lyer7o6TYO+2267Ffy8ZCcLZEG+1lhjjSZfy5QpUxa7X93+hh8GxtcSf64N2x2nRI9tjD+HuMUP6aLtttsu/OpXv0rns+42HTs36Fhki2xpqdiOuoJUPm3JcqEurUt20FIyQka0hbo+SPw8quFI8Dg1fSEjtWVK65EbAFBLsbuFHn300TT106J/oMW1NxtOMxqvEI1/eJ999tlpSqlFxWJl/OP03XffTWsiNSf+ERzXNJowYUI466yzQt++fdMVtnGNpjh98aLilZ4N21F3JW38g7+pq3Hj9HXxPkvqYBx22GFpVGn80DyuB9rww/Y4RVXsJMXz0fCq1HgFbnNrDMc/6OPUR3HKqNjBiB/ON/zjue5q0EWv/rz88stDPuI5q1vjqM748eMXu5o2XrEbz2m8SjauddVQ7KDEK3iXXXbZ9Mf8KqusUv+9OJXXhhtumP67YQHy9ddfr3/+pbUvim2M621FcR2m5q7Mje+R2Mnca6+90u045dhvf/vbdJVxfD/QtqpztVshx2cRp6KL05TFKcDilGzxA4B4JXp8n0bxKu541frYsWPT7fg7Jq4hFju68X0bR+7ECycOPvjgljeapZIFsqDQLIgXpMTXEqegrFu3O466XnRKx3ie4odcP//5z9OIkrr33K9//es0lWJci7FOly5d0gifP/zhD2H69OmNRnbHqc3jY8R2xenN6Ti5QcchW2RLodmyqDhDUDzm4osvTtMMLzqKO+ZOnDp3Sbp167ZY8YbWJztYGhkhI1o7I5bk61//emrD1VdfnUah17nyyisLelyZ0nrkBgDUUuxuoaOOOir9gb/nnnuGddZZJ12lGa80jX8sx1G2dVfHxj/a4x/KDT+EbigWOU899dRw0003hTFjxizxOeMf9rfcckv6AzteXRufP/6hHwtc8YPv2HGIfyzeeeed6arWODVTnMa4rh2xKNrctFOxHfE1xamh4rqezV2detttt6UP5uN9YhEtdh7i+kex4xOnnop/8MZ1l/IRp7eKhdv42mORN3740lAceRBHnV144YVpOtZY2IvrQMUrg/MR23f44Yenjln8ozx2xmInonfv3oud1z/96U/hm9/8ZjjggAPSh0GxPXFUbHy9sYMYj4mPF9dt2nHHHdM6SbGQ+Itf/CKd13XXXbdRZyCfdbR32WWXVMg86KCDUhvi++S6665LHzTF4kRTU1jF+8aprOO6UfG+ccqtOH0VHU9cnysWtOPV33FNsdiZje/fug5mfI80XDstXlUdp5eL/x7jv9X4Po6/k5rr4NM6ZIEsKDQL4odyv/vd79LPLk63GD/4iR+GxVEUcTrGOjEb4kUwcYRJvG/8WcVR3nH6xVjYXnSUSyxsx/dH/P0Rp6OsmwIxfvAYj4uvEShNskW2FJoti4p/M8aCUryQcv3110/vofia33nnnVQ4i+fjz3/+8xIfI7Y9Fjt+9rOfpYsrY6bE9gLtS0bIiNbOiCWJnz3FPsoll1ySflbx5x1fT7yoIbatpSO0ZQoA0OpytMh9992X++EPf5hbZ511ciuuuGJu+eWXz6211lq5o446Kjd79ux0n4ULF+ZWWWWV3Ne+9rUlPtbgwYNzm2yySfrvRx99NF5Tl7v11lubvO/w4cNzPXr0yH300Ue5zz//PPerX/0qN3LkyNwaa6yR69y5c26FFVZIj3XRRRflFixYkI6ZNGlSeszTTz+92Ta8+eab6T7HHnvsUl97vO+hhx6aW3311XPLLrtsOi5uDz30UC6rBx98MB1bVVWVmzFjxmLff/vtt3N77rlnbqWVVsr17Nkzt/fee+fefffddMyZZ55Zf7/rr78+7Zs2bVr9vurq6tyJJ56Y6927dzovI0aMyE2dOjWdq1GjRjV6no8//jh38sknp59h/FnGY4YNG5a7+OKL088xuu2223K77LJLbrXVVkv3ia//sMMOy82cObPRY8XHj1s+4s9mq622qn+8Sy+9tMnXEh9v9913z/3lL3/JbbjhhulnHd97zb1PaD1z5sxJP4/nX14t9/qMvi3e4vHxceLj0XHIAlnQGlnwz3/+M7f99tvnunTpkhswYEDunHPOyf36179e7LVEV155ZXq/Lbfccrk+ffrkfvSjH+U+/PDDxR7znnvuScfvuuuujfYffPDBaX98/Ibi88T98T2zqEXPM0smNyiUbJEthWZL3c86fm3oueeey+21117pvRN/pvGxvvvd7+YefvjhxY5d9H0ya9as1B/p3r17+n7MLVqP7CBfMkJGFJoRdW1+5plnmv1ew9fzxRdfpJ9h3759c127ds3tuOOOuVdeeSW9xw4//PClPm5TmSRTCic32u8cP/bYY8VuSodVlxPxK23DOW57znHbi7+H88mqqvh/rV9Cp5LEtYziFbZf/epX09WdcZpVWle8Qnvo0KHh7rvvLnZTKk6cLj6OjJz8cp+wYvf/G0md1byPa8Km681O0w3HK8Who5EFUEtuQOuRLVQK2QHZyYjiiSP544j7ODI7zhBA+5Mb7XeOH3vssTTbA60vzuoRZ3qYNGlSs7N7UBjnuO05x20vLg+z/fbbLzWrWp6G0GCqpLjOdJwCL06Z5foJOqKaXOEbdGSyABqTG1A42UKlkR2QPxnRPj799NPF9tWtXx7Xj6e45AYA1LJmN63ie9/7XtoAqFyyAIDWJlsAaI6MaHtxPfi4NnocRb/iiiuGJ554IvzhD38Iu+yyS9h2222L3TwAgESxGyAP1aEqbYUcD0DlkBsAZCU7gFKz4YYbhmWXXTZceOGFaVrnPn36hNGjR6cpzCk+uQEAtRS7oQy8+eabxW5CxdOBACALuQFAVrIDKDVx/dGHHnqo2M2gGXIDAGpZsxsAAAAAAACAsmNkN0AeanJVaSvkeAAqh9wAICvZAUAWcgMAMhS7a2pqwrvvvhu6d+8eqqqEIFDacrlc+Pjjj0P//v1Dp06tM4GFqaGykRtApWeH3MhOdgDlRJ+j+OQGUE7kBgAUudgdOw+DBg1qw2YAtL4ZM2aEgQMHtspjVYdOaWv58ZVFbgCVnh1yIzvZAZQjfY7ikRtAOZIbAFCkYne8SjY6YeRZ4YNp/2mDZnQcA77SP/zk2h+Fiw++Orzz2rvFbk7ZnK/zf35fePudD4vdnJI2cMDK4aSjdw2XHnldeGfq7GI3p6T1Xr1nOP/mk+t/d9H+5EYB2fGvmcVuTkkbsHa/dK5OeOy+8MZcubEkX+qxcrhw+13DOS/cFqZ/8kGxm1Py+uS6hV+MOFJ2FJHsyJ4bp93yQHjr334XLskaq6wcfvbdXcKlY24I777xXrGbU/L6f2m1MObS/cKFF98b3n7Hv8MlWbV3l3DxhQfJjSKSG9lz49KjfxPefd3nCUvT/8t9wpifj/LZXpb31nFydmlWGdg9nP/bn8gNAChWsbtuOqjYeZj+T3/kLcnyy3QOPXr0CO+//m/nKsP5eu+Dz8Kbb88tdnNK2vKdu9a+t978KEx/Sec0H605lV2uwHWQ4vGVRG5ks/yyXWr/fb/hfOWbGzO++CxM+VRuLMlyK9Tmxqyq+WFatWLYUi3TutkhN7KTHdl/F747f0GY+p95xW5OSVuuywrpXH0wfW6Y/sr7xW5O2fxN8t77n4a3psvZfOhzFI/cyJ4bH7w1J0x/WUEy7/6Zz/byf29NnxtmvOIC23zIDQAoUrEboNJZBwmALOQGAFnJDgCykBsAUEuxGyAP1blOaWv58a3aHABKnNwAICvZAUAWcgMAarU8DQEAAAAAAACgSIzsBshDTagKNQVcH1QTXC4LUEnkBgBZyQ4AspAbAFBLsRsgD9ZBAiALuQFAVrIDgCzkBgDUMo05AAAAAAAAAGVHsRsgD9W5TgVvAFQOuQFAVrIDgCzkRoWqqsq2QZkYPXp06Nu3b7Gb0aGN7sDn2DTmAHmvg9TyPxALORaA8iM3AMhKdgCQhdyoUGeeufi+yy8PYc6cpr8HZWLOnDlh9uzZxW5GhzanA59jxW6APNSETqG6gMkwakKuVdsDQGmTGwBkJTsAyEJuVKizzlp834QJtcXupr4HUAHMVQIAAAAAAABA2TGyGyAPha5lVJ1ztSxAJZEbAGQlOwDIQm5A26qpqQnHH398OOigg8J6661X7OYAS6DYDZDn1FBxa/nxOhAAlURuAJCV7AAgC7kBbae6ujrsv//+4cYbbwydOnUKF110UbGbBCyBYjcAAAAAAAAV74svvgg/+MEPwq233hp+/OMfhwsvvLDYTQKWQrEbIA/Vuaq0FXI8AJVDbgCQlewAIAu5AW1j7733DnfeeWfo2bNnqKqqCkcddVRex40ePTqsvfbabd4+YHGK3QB5qA6d0tby400NBVBJ5AYAWckOALKQG9A263RPnDgx/fecOXPCVVddlfex3/nOdxS7oUhanoYAFaQm16ngDYDKITcAyEp2AJCF3IDWF9fnfvjhh0OvXr3CSiutFP7+97+HXC6X1zZ8+PBiN7/sTJ48OYwbN26x/a+88kq44ooritKmjmZyhZxjI7sBAAAAAACoeJtuuml45JFHwk477RR23nnncN9994Vhw4YVu1kd0qmnnhruv//+8Mknn9Tve+mll8KOO+4YPv3007DXXnuFQYMGFbWN5e7UCjnHit0AeTA1FABZyA0AspIdAGQhN6DtbLTRRuHRRx8NX//619NIb8XutnHLLbeE3XbbLRx33HFh1VVXTft22GGHsHDhwvDAAw90iCJssd1SIedYsRsgDzWxE5CrKuh4ACqH3AAgK9kBQBZyA9rW0KFD0wjY3r17F7spHVb37t3TqOM99tgjXVwQffHFF+Ghhx4Km2++ebGb1yF0r5BzbGEOAAAAAAAAaEChu+1169Yt3HPPPWnK+FVWWSWNpO9IRdhS0K0CzrGR3QB5qAmd0lbI8QBUDrkBQFayA4As5Ab13nyz2C2AgnTt2jVNqU3b6drBz7FiN0AeqnOd0lbI8QBUDrkBQFayA4As5AYA1FLsBshDTahKWyHHA1A55AYAWckOALKQGwBQy+VbAAAAAAAAAJQdI7sB8mBqKACykBsAZCU7AMhCbgBALcVugDxUh05pK+R4ACqH3AAgK9kBQBZyAwBqSTQAAAAAAAAAyo5iN0AeanJVBW9ZXH311WHDDTcMPXr0SNs222wT7rvvviUec+utt4Z11lkndOnSJWywwQbh3nvvLfBVA1AuuQFA+ZMdAGQhNwCglmI3QB5q/js1VEu3eHwWAwcODOeff36YNGlSePbZZ8OOO+4Yvv3tb4eXXnqpyfs/+eST4fvf/3446KCDwnPPPRdGjhyZthdffLGVzgAApZwbAJQ/2QFAFnIDAGpZsxsgDzW5Tmkr5Pho7ty5jfZ37tw5bYvaY489Gt0+99xz02jvv/3tb2H99ddf7P5XXHFF+MY3vhGOP/74dPucc84JDz74YLjyyivDuHHjWtxuAIqbGwBUDtkBQBZyAwBqSTSAdjRo0KDQs2fP+m3s2LFLPaa6ujrcdNNNYf78+Wk686Y89dRTYaeddmq0b8SIEWk/AAAAAABAR2RkN0AeqkNV2go5PpoxY0Zag7tOU6O667zwwgupuP3ZZ5+FFVdcMdxxxx1hvfXWa/K+s2bNCn369Gm0L96O+wEo39wAoHLIDgCykBsAUEuxG6Adp4aKhe6Gxe4lGTJkSHj++efDnDlzwm233RZGjRoVHnvssWYL3gCUDlMKApCV7AAgC7kBALUUuwHyUF3gFa/x+KyWX375sNZaa6X/3myzzcIzzzyT1ua+5pprFrtv3759w+zZsxvti7fjfgAqIzcAKG+yA4As5AYA1HL5FkCZqKmpCQsWLGjye3G684cffrjRvgcffLDZNb4BAAAAAADKnZHdACU4NdTJJ58cdt1117D66quHjz/+ONx4441h4sSJ4S9/+Uv6/v777x8GDBgQxo4dm26PHj06bL/99uGSSy4Ju+++e7jpppvCs88+G8aPH9/iNgPQcqYUBCAr2QFAFnIDAGopdgPkoTrXKW2FHJ/Fe++9lwraM2fODD179gwbbrhhKnTvvPPO6fvTp08PnTr932MOGzYsFcRPO+20cMopp4S111473HnnnWHo0KEtbjMA5ZMbAJQ/2QFAFnIDAGopdgOUoF//+tdL/H4c5b2ovffeO20AVJ7q6upw1llnhRtuuCHMmjUr9O/fPxxwwAHpIqiqqpav4wcAABDpcwBQqhS7AfKQC1WhJlQVdDwAlaO9c+OCCy4IV199dfjNb34T1l9//bSUxYEHHphmBzn66KNb3A4A2o8+BwBZ6HMAQC1zlQBkmBqqkA2AytHeufHkk0+Gb3/722H33XcPa665ZvjOd74Tdtlll/D000+32WsEoHyzY+zYsWGLLbYI3bt3D6uttloYOXJkmDJlylKPu/XWW8M666wTunTpEjbYYINw7733FviqAWgpfQ4AqKX6ApCHmlxVwRsAlaO1cmPu3LmNtgULFjT5fMOGDQsPP/xweO2119Ltf/zjH+GJJ54Iu+66a7u+bgDKo8/x2GOPhSOOOCL87W9/Cw8++GD4/PPPU8Fi/vz5SyxyfP/73w8HHXRQeO6551KBPG4vvvhiK50BALLQ5wCAWqYxBwCAEjVo0KBGt88888y0Tt6iTjrppPTBVBxtt8wyy6T19M4999yw7777tmNrASgX999/f6PbEyZMSCO8J02aFLbbbrsmj7niiivCN77xjXD88cen2+ecc04qlF955ZVh3Lhx7dJuAFqfPgcA5U6xGyAP1aFT2go5HoDK0Vq5MWPGjNCjR4/6/Z07d27y/rfcckv4/e9/H2688ca0ft7zzz8fjjnmmNC/f/8watSoFrcDgPLLjliIaChmR3P5UWfOnDnpa69evZq9z1NPPRXGjBnTaN+IESPCnXfe2eI2A9By+hwAUEuxGyAPhU5FbhpzgMrSWrkRP3Rq+MFTc+IouzjS4nvf+166HddRfeutt9KarD54Aqis7Mh3hF79cTU1qVix7bbbhqFDhzZ7v1mzZoU+ffo02hdvx/0AtD99DgCopdgNAABl7pNPPgmdOjUe1RGnFowFDAAqS74j9OrEtbvjuttx3VUAaI4+BwClSrEbIA81oVPaCjkegMrR3rmxxx57pPXyVl999TSl4HPPPRcuvfTS8MMf/rDFbQCgPLMj3xF60ZFHHhnuvvvu8Pjjj4eBAwcu8b59+/YNs2fPbrQv3o77AWh/+hwAUEuxGyAP1bmqtBVyPACVo71z4xe/+EU4/fTTw49//OPw3nvvpXXzDjvssHDGGWe0uA0AdNzsyOVy4aijjgp33HFHmDhxYhg8ePBSj9lmm23Cww8/nKY8r/Pggw+m/QC0P30OAKil2A2QB2t2A1DKudG9e/dw+eWXpw2A8tSe2RGnLr/xxhvDXXfdlTKkbt3tnj17hq5du6b/3n///cOAAQPSWqzR6NGjw/bbbx8uueSSsPvuu4ebbropPPvss2H8+PEtbjMALafPAQC1zKsLAAAAUEGuvvrqMGfOnDB8+PDQr1+/+u3mm2+uv8/06dPDzJkz628PGzYsFchjcXujjTYKt912W7jzzjvD0KFDi/QqAAAAjOwGyEsu1ynU5DoVdDwAlUNuAFDK2RGnMV+aOL35ovbee++0AVB8+hwAUEuxGyAP1aEqbYUcD0DlkBsAZCU7AMhCbgBALZdvAQAAAAAAAFB2jOwGyENNLm5VBR0PQOWQGwBkJTsAyEJuAEAtxW6APNQUuA5SIccCUH7kBgBZyQ4AspAbAFBLsRsgDzWhKm2FHA9A5ZAbAGQlOwDIQm4AQC2XbwEAAAAAAABQdozsBshDda4qbYUcD0DlkBsAZCU7AMhCbgBALcVugDxYBwmALOQGAFnJDgCykBsAUEuxGwAAAAAAoIEpU6aEFVdcsdjN6JBeeeWVRl9pfc5x23OO2+f3cD4UuwHyUBOqQk0B0zvF4wGoHHIDgKxkBwBZyI3Wd9VVV6Wturo63T700EOL3aQOb7/99it2Ezo857jtOcfFp9gNkIdc7EAU0AmIxwNQOeQGAFnJDgCykBut74gjjkjb3LlzQ8+ePcP48ePDZpttVuxmdUhxJGwsEN5www1h3XXXLXZzOiTnuO05x21v0qRJeV14pNgNkId4pWxBV8sWcCwA5UduAJCV7AAgC7nR9oYMGRI23XTTYjejQ4sFQue4bTnHbc85bjvz5s3L636d2rANAAAAAAAAANAmjOwGyENNrlPaCjkegMohNwDISnYAkIXcAIBait0AeTA1FABZyA0AspIdAGQhNwCglmI3QB5qQlXaCjkegMohNwDISnYAkIXcAIBa5ioBAAAAAAAAoOwY2Q2QB1NDAZCF3AAgK9kBQBZyAwBqKXYD5EEHAoAs5AYAWckOALKQGwBQyzTmAAAAAAAAAJQdI7sB8uBqWQCykBsAZCU7AMhCbgBAC4rdvX/w1fDFrp9lOaTirNq/V/q6xiZfDp26rVDs5pS8QV/pl75+ecUVw/I9vbeWZNCKK6avA9dcNeQWfF7s5pS0VVdfOYSnWvcxdSBaZtCma4euq6xW7GaUvIFr901f19j8K2GZnj2K3ZyyOFfD18mFtT7NFbs5JW1g19rzc9Dgf4c5C2cVuzklb9kFIdzSio8nN1pu0Nbrh64DVy92M0rawC/XZuuQriuHzt38LlySNbuunL4OHNAz5OZ/UuzmlLx4nqIvH90pdP7URHBL0ru6UwjXte5jyo6WGbjOoNC5c21/maYNGtI/fe3fv3uonrtSsZtT8uJ5ivrt/dXw+TbrFbs5Ja3fwNrPQQdsuXao6VP73zRt1b5dQ3ikdR9TbgBAC4rdpx4+IvTo4UP4fJx07WHFbkJZOfln/1PsJpSNk397RLGbUPLmzp0bzr75+FZ9zPgxck1oeSegUj+GHnP5/nIjg5PGHVTsJpSN49c9oNhNKBvD+51V7CaUTXaEcH6rPZ7caLkxF35PduTp3EN3K3YTysZJ1/+o2E0oKyeuO6rYTSiL3DgrtO77Sna0zHHjD5UbeTrl96OL3YSycsZJ3yp2E8rGyef4XC+vz6ouat3HlBsA0IJi97nj/hJmfWj07ZKs0b9XOPuo3cP5B18TZrw2s9jNKYuR3fHCgLGn3R5mvPlBsZtT0gat2TtdFDB2/6vCjCnvFrs5JW3VwbUjeCi+S4/5bfjgrTnFbkZZjFaOhe7zD/91ePtfRuDmc64uemVCePvT2cVuTkkb2LVPuihg4syzwpyFbxW7OSVv2QW1swZQfJeecFP44O2Pi92Mkh/ZfdIV/xtOHX9veHPWf4rdnJK2Zt9e6aKA8w+8Wv8s3/7Z9T8KF7zyGzm7FL2rFVdLxSWHjg/vT/uw2M0o+ZHd8cL58/a9Isx49Z1iN6fkDVpnQLow4Ozz/xSmvy1nl2T1gb3SRQFjT789zHjr38VuTumP7AYAil/snj7rwzBtpg+e8hE/SJn6j+nFbkbZiIXuqVN8+JSPWOie+vybxW5GSVtYvaDVH9PUUC3z7uuzw/RX3i92M8pGLHRPfWFGsZtRFuIH8K/Pf7vYzSgLsdD97wWvFbsZJa/rwoWt+nhyo+XenfZ+mPGaD0vzEQvdr05/r9jNKKP+mQt/MuXsPDm7JAtD71Z/TNnRMu9MnRWmv+DzhHzEQvfU56YVuxllIxa6//W6C3/yEQvdU6e4cHtJFi6sXSqkNckNAGhBsRugUulAAJCF3AAgK9kBQBZyAwBqdfrvVwBKxNixY8MWW2wRunfvHlZbbbUwcuTIMGXKlCUeM2HChFBVVdVo69KlS7u1GQAAAAAAoL0Z2Q1QYlfLPvbYY+GII45IBe8vvvginHLKKWGXXXYJL7/8cujWrVuzx/Xo0aNRUTwWvAEoDqMsAMhKdgCQhdwAgFqK3QAl1oG4//77Fxu1HUd4T5o0KWy33XbNHheL23379m1xGwFoPT54AiAr2QFAFnIDAGopdgPkIZerSlshx0dz585ttL9z585pW5I5c+akr7169Vri/ebNmxfWWGONUFNTEzbddNNw3nnnhfXXX7/FbQag+LkBQOWQHQBkITcAoJY1uwHa0aBBg0LPnj3rt7g+95LEwvUxxxwTtt122zB06NBm7zdkyJBw3XXXhbvuuivccMMN6bhhw4aFt99+uw1eBQAAAAAAQPEZ2Q2Qh5pQlbZCjo9mzJiR1taus7RR3XHt7hdffDE88cQTS7zfNttsk7Y6sdC97rrrhmuuuSacc845LW43AMXNDQAqh+wAIAu5AQC1FLsB2nEdpFjobljsXpIjjzwy3H333eHxxx8PAwcOzPR8yy23XNhkk03C1KlTW9ReAApj/TwAspIdAGQhNwCglmnMAUpMLpdLhe477rgjPPLII2Hw4MGZH6O6ujq88MILoV+/fm3SRgAAAAAAgGIzshsgD7lcVdoKOT5fceryG2+8Ma2/3b179zBr1qy0P67x3bVr1/Tf+++/fxgwYED9mt9nn3122HrrrcNaa60VPvroo3DRRReFt956Kxx88MEtbjMA5ZEbAHQMsgOALOQGANRS7AYosamhrr766vR1+PDhjfZff/314YADDkj/PX369NCp0/9NzvHhhx+GQw45JBXGV1555bDZZpuFJ598Mqy33notbjMALWdKQQCykh0AZCE3AKCWYjdAiV0tG6cxX5qJEyc2un3ZZZelDYDSYJQFAFnJDgCykBsAUMua3QAAAAAAAACUHSO7AfK82rWQ6Z1cLQtQWeQGAFnJDgCykBsAUEuxGyAPcWLxPGYXX+LxAFQOuQFAVrIDgCzkBgDUUuwGyENNqEr/K+R4ACqH3AAgK9kBQBZyAwBqWbMbAAAAAAAAgLJjZDdAnusYFbKWkXWQACqL3AAgK9kBQBZyAwBqKXYD5KEmVxWqCugExOMBqBxyA4CsZAcAWcgNAKhlGnMAAAAAAAAAyo5iN0AecrnCNwAqh9wAICvZAUAWcoPoiy++CPvuu29YZ511wmuvvVbs5kCLjR49OvTt27fYzejQRnfgc2wac4A8WAcJgCzkBgBZyQ4AspAbLFy4MOyzzz7hzjvvTLeHDx8eHnnkkVT4hnIzZ86cMHv27GI3o0Ob04HPsZHdABk6EIVsAFQOuQFAVrIDgCzkRmVbsGBB2GuvvVKhu0ePHmnfvHnzUsH7pZdeKnbzANqVYjcAAAAAAECZiCO677nnnnDyySeHPffcM+174IEHwqeffhp22GGH8Pbbbxe7iQDtRrEbIA81uaqCNwAqh9wAICvZAUAWcqOyHXPMMeFnP/tZOO+88+r3bb311uHBBx8Mhx12WBg4cGBR29cR1NTUhOOOOy68/PLLxW4KsBTW7AbIQy5XuxVyPACVQ24AkJXsACALuVHZ4nTlcVvUlltumTYKU11dHfbff/9w4403hk6dOoWLLrqo2E0ClkCxGwAAAAAAgIr3xRdfhB/84Afh1ltvDT/+8Y/DhRdeWOwmAUuh2A2Q99WyLZ/eydWyAJVFbgCQlewAIAu5AW1j7733DnfeeWfo2bNnqKqqCkcddVRex40ePTqsvfbabd4+YHGK3QB5iJ2HwjoQ1kECqCRyA4CsZAcAWcgNaJt1uidOnJj+e86cOeGqq67K+9jvfOc7it1QJJ2K9cQA5STXChsAlUNuAJCV7AAgC7kBrS+uz/3www+HXr16hZVWWin8/e9/D7lcLq+tqTXUWbLJkyeHcePGLbb/lVdeCVdccUVR2tTRTK6Qc2xkNwAAAAAAABVv0003DY888kjYaaedws477xzuu+++MGzYsGI3q0M69dRTw/333x8++eST+n0vvfRS2HHHHcOnn34a9tprrzBo0KCitrHcnVoh51ixGyAPpoYCIAu5AUBWsgOALOQGtJ2NNtooPProo+HrX/96Gumt2N02brnllrDbbruF4447Lqy66qpp3w477BAWLlwYHnjggQ5RhC22WyrkHCt2A+Sj0PmdzA0FUFnkBgBZyQ4AspAb0KaGDh2aRsD27t272E3psLp3755GHe+xxx7p4oLoiy++CA899FDYfPPNi928DqF7hZxja3YDAEAH8M4774T99tsvrLLKKqFr165hgw02CM8++2yxmwUAAHQQ+hxUGoXuttetW7dwzz33pCnj4++WOJK+IxVhS0G3CjjHRnYD5KPAqaHi8QBUkHbOjQ8//DBsu+22aSqquJ5YnJrqX//6V1h55ZVb3gYA2pc+BwBZ6HPwXxMmTEgblKt48UycUpu207WDn2PFboA85HK1WyHHA1A52js3LrjggrTO0vXXX1+/b/DgwS1vAAAdPjsef/zxcNFFF4VJkyaFmTNnhjvuuCOMHDmy2ftPnDgxFTgWFY/t27dvS5oMQAH0OQCglmnMAfIQr5QtdAOgcrRWbsydO7fRtmDBgiaf709/+lOagmrvvfcOq622Wthkk03Cr371q3Z+1QCUU59j/vz5YaONNgpXXXVVpuOmTJmSCtx1W8wdANqfPgcA1FLsBgCAEhVHTvTs2bN+Gzt2bJP3e+ONN8LVV18d1l577fCXv/wl/OhHPwpHH310+M1vftPubQagPOy6667hZz/7Wdhzzz0zHRcLHHEkd93WqZOPlgDKmT4HAOXONOYA+YhXu1o/D4B2zo0ZM2aEHj161O/u3Llzk3evqalJoyzOO++8dDuOsnjxxRfDuHHjwqhRo1reDgDKLjviqLyGYnY0lx8tsfHGG6dRf0OHDg1nnXVWWr8VgCLQ5wCAxOW3ABnWQSpkA6BytFZuxA+dGm7NffDUr1+/sN566zXat+6664bp06e3x8sFoISyI98RelnFrIkFjdtvvz1t8XmGDx8eJk+e3CqPD0A2+hwAUMvIbgAAKHNxVF1cQ7Wh1157LayxxhpFaxMAxZHvCL2shgwZkrY6w4YNC6+//nq47LLLwu9+97tWeQ4ASpc+BwClSrEbIB/xatdCRmcb2Q1QWdo5N4499thUdIhTCn73u98NTz/9dBg/fnzaAKis7Kgbmdcettxyy/DEE0+0y3MBsAh9DgBIFLsB8pDLVaWtkOMBqBztnRtbbLFFuOOOO8LJJ58czj777DB48OBw+eWXh3333bfFbQCgfZVjn+P5559P09oC0P70OQCglmI3QL6MzgaghHPjm9/8ZtoAKGPtmB3z5s0LU6dOrb89bdq0VLzu1atXWH311VMx45133gm//e1v0/djQSMWNtZff/3w2WefhWuvvTY88sgj4YEHHmi/RgPQmD4HACh2AwAAAFSaZ599Nuywww71t8eMGZO+jho1KkyYMCHMnDkzTJ8+vf77CxcuDMcdd1wqgK+wwgphww03DA899FCjxwAAAGhvit0AHXRKQQCKR24AUOrZMXz48JDLNT8kMBa8GzrhhBPSBkBp0OcAgFqK3QD5yBU4NZQp0AEqi9wAICvZAUAWcgMAEsVugLzEq10LueLV1bIAlUVuAJCV7AAgC7kBAFEnpwEAAAAAAACAcmNkN0A+TA0FQBZyA4CsZAcAWcgNAEiM7AbI0oEoZMvT2LFjwxZbbBG6d+8eVltttTBy5MgwZcqUpR536623hnXWWSd06dIlbLDBBuHee+8t7DUDUBa5AUAHITsAyEJuAECi2A1QYh577LFwxBFHhL/97W/hwQcfDJ9//nnYZZddwvz585s95sknnwzf//73w0EHHRSee+65VCCP24svvtiubQcAAAAAAGgvpjEHyEeuqnYr5Pg83X///Y1uT5gwIY3wnjRpUthuu+2aPOaKK64I3/jGN8Lxxx+fbp9zzjmpUH7llVeGcePGtbzdAJR8bgDQQcgOALKQGwCQKHYD5CGXq90KOT6aO3duo/2dO3dO25LMmTMnfe3Vq1ez93nqqafCmDFjGu0bMWJEuPPOO1veaACKnhsAVA7ZAUAWcgMAapnGHKAd10EaNGhQ6NmzZ/0W1+dekpqamnDMMceEbbfdNgwdOrTZ+82aNSv06dOn0b54O+4HoAisnwdAVrIDgCzkBgAkRnYDtKMZM2aEHj161N9e2qjuuHZ3XHf7iSeeaIfWAQAAAAAAlA/FboB2XAcpFrobFruX5Mgjjwx33313ePzxx8PAgQOXeN++ffuG2bNnN9oXb8f9ABSB9fMAyEp2AJCF3ACAxDTmAHmoyhW+5SuXy6VC9x133BEeeeSRMHjw4KUes80224SHH3640b4HH3ww7QegY+cGAB2D7AAgC7kBALWM7AYoMXHq8htvvDHcddddoXv37vXrbsc1vrt27Zr+e//99w8DBgyoX/N79OjRYfvttw+XXHJJ2H333cNNN90Unn322TB+/PiivhYAAAAAAIC2YmQ3QD5yrbDl6eqrrw5z5swJw4cPD/369avfbr755vr7TJ8+PcycObP+9rBhw1KBPBa3N9poo3DbbbeFO++8MwwdOrS1zwQAJZYbAHQQsgOALOQGACRGdgOU2DpIcRrzpZk4ceJi+/bee++0AVACrJ8HQFayA4As5AYAJIrdAPko9IpXV8sCVBa5AUBWsgOALOQGACSmMQcAAAAAAACg7BjZDZAPV8sCkIXcACAr2QFAFnIDABLFboB86EAAkIXcACAr2QFAFnIDABLTmAMAAAAAAABQdozsBshHrqp2K+R4ACqH3AAgK9kBQBZyAwASxW6APFTlardCjgegcsgNALKSHQBkITcAoJZiN0A+rIMEQBZyA4CsZAcAWcgNAEis2Q0AAAAAAABA2VHsBgAAAAAAAKDsmMYcIA9VBa5lFI8HoHLIDQCykh0AZCE3AKAFxe4vd+seevRUH1+SQSv2SF8HrrlqyC34vNjNKXnxPEWr91spdPrM+VqSgf1WSl+vvP+TEKrnFbs5JW3u/E/Cr9csdiuI+q+xSljedVVLNXBw7/qvuc/9LsznXHWbtEHoMWtAsZtT0rr17RXCpiHs0WOlEKprzxvNmzu/Z7GbwH/169U5LNt3hWI3o6QNWKVL+rrB/OXCynM7F7s5JW3g/OXS11W+t1WYt93axW5OyVulf6/0dd/+c8LHn/+72M0paZ0+Wz6ML3YjSLoeuXVY8ZMFxW5GSVuh98rp61duHBK6fVr72QLNG9C1T/r649NfCfM+n1bs5pS0FZcbnL72Oq5H6DPfv8MlWTnXLYSbi90KspoyZUpYccUVi92MDumVV15p9JXW5xy3Pee4fX4P56Mql8st9fqvuXPnhp49e4Y5c+aEHj1qi7kApao1f2fVPdYa558bOnWp/WC5JWo++yy8ddKpFfN7VG4Alfp7S260nOwAyok+R/HJDaCcyI3ycNVVV6Wturo6vPbaa8VuDgD/tbSsyjTc7sKL7gnvv/9plkMqzqBBq4TTTv12GLv/VWHGlHeL3ZySN2hI/3Dyb48I5x9/c3h72vvFbk5JGzh41XDSRfuEmg+PDaH6jWI3p6TVzG+D0Z7xsqACpoYq6NgydumR14UP3ppT7GaUvIFr9w0nXXtYOP/ga8KM12YWuzklbdBX+qVzdcqv7g1vzvpPsZtT0tbs2yucd8hucqNY2SE3WuySw8aH99/8qNjNKPnfhSf/5ohw/onxb+gPit2ckp8R5KQL9gln/OKe8Na7cmNp1ujfK5x91O7hb7NODR9//maxm1PSOn3Wr/UfVHa0yKl/fCC8Y2T3En2p98rh4u/uFi6b8uvwzqezi92cshjZfeyQg8Lk2Sca2Z3HyO5N+1wQTvvHTeGt+T7XW5K+cWR3a5Mbre6II45IW90FBePHjw+bbbZZsZvVIcWRsPvtt1+44YYbwrrrrlvs5nRIznHbc47b3qRJk8Khhx661PtlKna/885/wltvzS2kXRUjFrqnPu/DgXzFQvfUV1wckJdYsPji5WK3orTVtME00DoQLfLu67PD9JfeK3YzSl7dJCux0D31H9OL3ZyyEAvdr0733sqL3ChOdsiNFntn6uww/UUX/uQjFrqnvuJc5SMWuqe8KTfyFQvdHy14tdjNKGnLtcXSM7KjRd7894fhXx9Z6isfsdD9xvwZxW5G2YiF7jkLTUuaj1jonjLX53pLsnCZ2uUEWpXcaHNDhgwJm266abGb0aHFAqFz3Lac47bnHLedefPy+zu/Uxu2AQAAAAAAAADaRKaR3QCVqipXuxVyPACVQ24AkJXsACALuQEAtRS7AfJhaigAspAbAGQlOwDIQm4AQKLYDZAPHQgAspAbAGQlOwDIQm4AQGLNbgAAAAAAAADKjpHdAHmwDhIAWcgNALKSHQBkITcAoJZiN0A+clW1WyHHA1A55AYAWckOALKQGwCQmMYcAAAAAAAAgLJjZDdAPuLUToVM72RqKIDKIjcAyEp2AJCF3ACARLEbIA/WQQIgC7kBQFayA4As5AYA1FLsBsiHq2UByEJuAJCV7AAgC7kBAIk1uwEAAAAAAAAoO0Z2A+SjwKmhXC0LUGHkBgBZyQ4AspAbAJAodgPkw9RQAGQhNwDISnYAkIXcAIDENOYAAAAAAAAAlB0juwHy4WpZALKQGwBkJTsAyEJuAECi2A2Qh6oC10EqaA0lAMqO3AAgK9kBQBZyAwBqmcYcAAAAAAAAgLKj2A0AAAAAAABA2TGNOUA+rIMEQBZyA4CsZAcAWcgNAEgUuwHyYB0kALKQGwBkJTsAyEJuAEAt05gDAAAAAAAAUHaM7AbIlyteAchCbgCQlewAIAu5AQBGdgNkWgepkC2Dxx9/POyxxx6hf//+oaqqKtx5551LvP/EiRPT/RbdZs2aVdjrBqAscgOADkB2AJCF3ACAxMhugBJcB2n+/Plho402Cj/84Q/DXnvtlfdxU6ZMCT169Ki/vdpqq2V7YgBahfXzAMhKdgCQhdwAgFqK3QAlaNddd01bVrG4vdJKK7VJmwAAAAAAAEqJacwB2nFqqLlz5zbaFixY0KrN3HjjjUO/fv3CzjvvHP7617+26mMDkIEpBQHISnYAkIXcAIBEsRsgw9RQhWzRoEGDQs+ePeu3sWPHtkr7YoF73Lhx4fbbb09bfJ7hw4eHyZMnt8rjA1Cc3ACgcsgOALKQGwBQS7EboB2vlp0xY0aYM2dO/XbyySe3SvOGDBkSDjvssLDZZpuFYcOGheuuuy59veyyy1rl8QEor1EW559/fqiqqgrHHHNMa70iANqaEXoAlElu6G8AUEqs2Q3Qjnr06JG29rDllluGJ554ol2eC4DS8cwzz4RrrrkmbLjhhsVuCgAA0MHobwBQaozsBuigoyyef/75NL05AJWTG/PmzQv77rtv+NWvfhVWXnnl1n5VAHSg7Hj88cfDHnvsEfr3759G5915551LPWbixIlh0003DZ07dw5rrbVWmDBhQstfLwBl1+fQ3wCgFCl2A+ShvddBip2HWKyOWzRt2rT039OnT0+34/Tn+++/f/39L7/88nDXXXeFqVOnhhdffDFNI/XII4+EI444onVPBADtmhtz585ttC1YsGCJzxt/7+++++5hp512ap8XCkDZ9jnmz58fNtpoo3DVVVfldf/YJ4kZs8MOO6S+SexzHHzwweEvf/lLy14wAGXX59DfAKAUmcYcoAQ9++yz6UOkOmPGjElfR40alUZPzJw5s77wHS1cuDAcd9xx4Z133gkrrLBCmkrqoYceavQYAJSfQYMGNbp95plnhrPOOqvJ+950001h8uTJaVpBACpXLFQ0FEdhx21Ru+66a9ryNW7cuDB48OBwySWXpNvrrrtuWjbpsssuCyNGjGiFlgNQyn0O/Q0ASpViN0A+Cp2KPOOxw4cPD7lc8wctOl3gCSeckDYAOlZuzJgxI/To0aN+d1PFirr7jR49Ojz44IOhS5cuBTwxAOWeHVkulMriqaeeWmwkXyxyxxHeAHTsPof+BkD5Gz16dLj55pvDrFmzQkej2A1QgsVuAMpcK+VG/NCp4QdPzZk0aVJ477330jqqdaqrq9N6rFdeeWWainCZZZYpoEEAdLQLpbKKH4r16dOn0b54O44k//TTT0PXrl1b5XkAKL0+h/4GQPmbM2dOmD17duiIFLsB8tCSNfAWPR6AytHeufH1r389vPDCC432HXjggWGdddYJJ554og+eACooO/K9UAqA8taefQ79DQBKmWI3AACUue7du4ehQ4c22tetW7ewyiqrLLYfAFqib9++i40EibdjYd2oboCOTX8DgFLWqdgNACirqaEK2QCoHHIDgA6WHdtss014+OGHG+2La7fG/QAUQYnnBsWxcOHCcOSRR4a33nqr2E3psJzjtucck5ViN0CGqaEK2QCoHKWQGxMnTgyXX355a7wcADpgdsybNy88//zzaYumTZuW/nv69Onp9sknnxz233//+vsffvjh4Y033ggnnHBCePXVV8Mvf/nLcMstt4Rjjz22dU8EAGXR59DfKE2TJ08O1157bdhuu+3C66+/XuzmdEjOcdtzjslKsRsAAACgwjz77LNhk002SVs0ZsyY9N9nnHFGuj1z5sz6wnc0ePDgcM8996TR3BtttFG45JJL0oeQI0aMKNprAAAa23rrrcOf//zn8P7776dC4ZQpU4rdpA7HOW57zjFZWbMbIB+FTu9kZDdAZZEbAJR4dgwfPjzkcs0fNGHChCaPee6551rSOgBamz5HxTn99NPDhx9+mNd941rqzzzzTNh+++3DY489FoYMGdLm7esInOO25xzTFhS7AfKhAwFAFnIDgKxkBwBZyI2Kc/3114d33nkn0zGzZ88OL7/8siJhnpzjtucc0xZMYw6Qh6pW2ACoHHIDgKxkBwBZyI3K8/bbb6dZWZa2zZs3L83GEv30pz8Ne+65Z7GbXjac47bnHLf/+ufjxo1bbP8rr7wSrrjiitBRGNkNAAAAAABQ5j7++OOw2267hSeeeCKcf/754cQTTyx2kzoc57jtOcet59RTTw33339/+OSTT+r3vfTSS2HHHXcMn376adhrr73CoEGDQrlT7AbIh6mhAMhCbgCQlewAIAu5QROmTp0aXnjhhXDppZeGY489ttjN6ZCc47bnHLeeW265JV04cNxxx4VVV1017dthhx3CwoULwwMPPNAhCt2RYjdAHqpytVshxwNQOeQGAFnJDgCykBs0ZZNNNkmFwt69exe7KR2Wc9z2nOPW07179zSye4899giPPvpo2vfFF1+Ehx56KGy++eaho7BmNwAAAAAAQAegQNj2nOO25xy3nm7duoV77rkn7LzzzmGVVVYJDz/8cIcqdEdGdgPkw9RQAGQhNwDISnYAkIXcACBPXbt2TdOWd1SK3QD50gkAIAu5AUBWsgOALOQGACh2A+TDOkgAZCE3AMhKdgCQhdwAgFrW7AYAAAAAAACg7BjZDZAP6yABkIXcACAr2QFAFnIDABLFboA8mBoKgCzkBgBZyQ4AspAbAFDLNOYAAAAAAAAAlB0juwHyYWooALKQGwBkJTsAyEJuAECi2A2QB1NDAZCF3AAgK9kBQBZyAwBqKXYD5MPVsgBkITcAyEp2AJCF3ACAxJrdAAAAAAAAAJQdI7sB8uFqWQCykBsAZCU7AMhCbgBAotgNkAfrIAGQhdwAICvZAUAWcgMAail2A+TD1bIAZCE3AMhKdgCQhdwAgMSa3QAAAAAAAACUHSO7AfJQlculrZDjAagccgOArGQHAFnIDQCopdgNkA9TQwGQhdwAICvZAUAWcgMAEtOYAwAAAAAAAFB2jOwGyENVrnYr5HgAKofcACAr2QFAFnIDAGopdgPkw9RQAGQhNwDISnYAkIXcAIBEsRsgD66WBSALuQFAVrIDgCzkBgDUsmY3AAAAAAAAAGXHyG6AfJgaCoAs5AYAWckOALKQGwCQKHYD5MHUUABkITcAyEp2AJCF3ACAFhS7V//k09B13rwsh1ScQZ/0SF87n7pt6PqfdYvdnJLXuVev9LXT0auHZeZ2K3ZzSlqnHqukr+eP3jHMeHVIsZtT0lb9cjxX9xW7GYQQ+q/VNyy/XNdiN6Pk9d154/S134hNw+frrVHs5pS0foNqfxf2HdorfNKv2K0pbX1Xqc1YuZEf2VE6Bm41JHQZOLDYzShpA7+0avra5X8Gh24frFTs5pS0Lr1XTl+vP2FyCNVvFLs5pW+ZL4UQ/jc8c9KWYcarA4rdmjLIjVuL3Qzi78QNu4VQY5W+JVm9e+3nLTuuGsLGPYvdmtLXa/nar1/tNiCELguK3ZzStkxtVqx7VU1Y8dXqYrempK36ZZVlACiJYvdx4w8NPXrUFnNZssu+tVuxm1BWfr7dt4rdhLJxyu9HF7sJJW/u3LnhnFtPKuupoR5//PFw0UUXhUmTJoWZM2eGO+64I4wcOXKJx0ycODGMGTMmvPTSS2HQoEHhtNNOCwcccEAopjG/OFBuZHDaqX4X5uvSPeVsvuRGkbLDlIItNua878qOPF28j9+F+eq08mXFbkJZkR2V0efoKC7Y6ptyI0+7DTil2E0oK7Ijf3Jj6eQGAJRIsfuSQ8eH96d92Hat6QAGDekfTv7tEeHYP90b3vjPf4rdnJL3pV690oUBRz/+p/D63H8Xuzkl7cs9VkkXBZy37xVhxqvvFLs5ZTDKovW15/RO8+fPDxtttFH44Q9/GPbaa6+l3n/atGlh9913D4cffnj4/e9/Hx5++OFw8MEHh379+oURI0aEYrn0qOvDB2/NKdrzl9PI7ljo/tm5fwrTZ/hduCSrD1olnasxd9wb3vi3nF2SL63SK10UIDeKlx2mBWyZS0+5JXzwzvxiN6PkR3afdMkPwk9uvjdM+0D/bEkG9145XRRQ8+GxRnbnY5kvpeKO7KiMPkdHceLf7w5v13xa7GaUtC93XyVcPmxkuPed88J/Fs4odnNKXq/lB6ULA2RHHuRG3uQGAJRIsfudqbPC9Bdmtl1rOpBY6H5p9nvFbkbZiIXul/4zu9jNKAux8zD1uWnFbkZJW1jdBtOM5XK1WyHHZ7DrrrumLV/jxo0LgwcPDpdcckm6ve6664YnnngiXHbZZUUtdr/7+uww/WW/C5dm4TqD0tdY6P7XVL8L8xEL3S/P8t7Kh9woUna0c250JO+++e8w/V8uZslHLHS/PNPvwrzEYsUXLxe7FWVDdlRGn6OjmPbxf8JrC11gm49Y6H7vs6nFbkb5kB15kxtLJzcAoO1Y1AignaetargtWNA6nZ2nnnoq7LTTTo32xSJ33A8AAAAAANARKXYD5DktVKFbFNfS7tmzZ/02duzYVmnfrFmzQp8+fRrti7djQf3TT03pB1CuuQFA5ZAdAGQhNwCgBdOYA1Ss2AEopBPw32NnzJgRevToUb+7c+fOhbcNgA6bGwBUENkBQBZyAwASxW6AdhQL3Q2L3a2lb9++Yfbsxms9x9vxubp27drqzwcAAAAAAFBsit0Aeaiqqd0KOb4tbbPNNuHee+9ttO/BBx9M+wFof6WeGwCUHtkBQBZyAwBqWbMbIMvUUIVsGcybNy88//zzaYumTZuW/nv69Onp9sknnxz233//+vsffvjh4Y033ggnnHBCePXVV8Mvf/nLcMstt4Rjjz22dc8DACWZGwB0ALIDgCzkBgAkRnYD5KEqV7sVcnwWzz77bNhhhx3qb48ZMyZ9HTVqVJgwYUKYOXNmfeE7Gjx4cLjnnntScfuKK64IAwcODNdee20YMWJEyxsNQNnkBgDlT3YAkIXcAIBait0AJWj48OEhl2u+1xEL3k0d89xzz7VxywAAAAAAAEqDYjdAPmLheQnF57yOB6ByyA0AspIdAGQhNwAgUewGyIOpoQDIQm4AkJXsACALuQEAtRS7AQAAAAAAGpgyZUpYccUVi92MDumVV15p9JXW5xy3Pee4fX4P50OxGyAf8WrXQq54dbUsQGWRGwBkJTsAyEJutLqrrroqbdXV1en2oYceWuwmdXj77bdfsZvQ4TnHbc85Lj7FboA8mBoKgCzkBgBZyQ4AspAbre+II45I29y5c0PPnj3D+PHjw2abbVbsZnVIcSRsLBDecMMNYd111y12czok57jtOcdtb9KkSXldeKTYDZCPXK52K+R4ACqH3AAgK9kBQBZyo80NGTIkbLrppsVuRocWC4TOcdtyjtuec9x25s2bl9f9OrVhGwAAAAAAAACgTRjZDZAHU0MBkIXcACAr2QFAFnIDAGopdgPkI3YACukE6EAAVBa5AUBWsgOALOQGACSK3QB5cLUsAFnIDQCykh0AZCE3AKCWNbsBAAAAAAAAKDtGdgPkoyZXuxVyPACVQ24AkJXsACALuQEAiWI3QD6sgwRAFnIDgKxkBwBZyA0ASExjDgAAAAAAAEDZMbIbIA9VccsVdjwAlUNuAJCV7AAgC7kBALUUuwHykcvVboUcD0DlkBsAZCU7AMhCbgBAotgNkId4pWxBV8vqPwBUFLkBQFayA4As5AYA1LJmNwAAlLmxY8eGLbbYInTv3j2sttpqYeTIkWHKlCnFbhYAJe6qq64Ka665ZujSpUvYaqutwtNPP93sfSdMmBCqqqoabfE4ACqDPgcApUqxGyAfuVbYAKgc7Zwbjz32WDjiiCPC3/72t/Dggw+Gzz//POyyyy5h/vz5bfUKASjz7Lj55pvDmDFjwplnnhkmT54cNtpoozBixIjw3nvvNXtMjx49wsyZM+u3t956q/DXDUDL6HMAQGIac4A8VOVyaSvkeAAqR2vlxty5cxvt79y5c9oWdf/99y82+i6Otpg0aVLYbrvtWtwOADpun+PSSy8NhxxySDjwwAPT7XHjxoV77rknXHfddeGkk05q+jmqqkLfvn1b3EYAWo8+BwDUMrIbAABK1KBBg0LPnj3rtzh1YD7mzJmTvvbq1auNWwhAqYlFi4bbggULFrvPwoULU3Fip512qt/XqVOndPupp55q9rHnzZsX1lhjjZRP3/72t8NLL73UZq8DgPahzwFAuTOyGyAfNf/dCjkegMrRSrkxY8aMNGVsnaZGWCx2aE1NOOaYY8K2224bhg4dWkAjACjH7IhFi4biNOVnnXVWo30ffPBBqK6uDn369Gm0P95+9dVXm3z4IUOGpFHfG264YSpwXHzxxWHYsGGp4D1w4MACGg5Ai+hzAECi2A2QB9OYA1CM3IgfOjX84CkfcR29F198MTzxxBMtfn4Ayjc7WlK0yMc222yTtjqx0L3uuuuGa665Jpxzzjmt8hwA5E+fAwBqKXYD5CP+/V9IvVqtG6CyFCk3jjzyyHD33XeHxx9/3Cg7gArNjnyKFr179w7LLLNMmD17dqP98Xa+a3Ivt9xyYZNNNglTp05teZsBaDl9DgBIrNkNAABlLpfLpQ+d7rjjjvDII4+EwYMHF7tJAJSw5ZdfPmy22Wbh4YcfbjQlbbzdcPT2ksRp0F944YXQr1+/NmwpAKVCnwOAUmVkN0A+4tROhUxFbhpzgMrSzrkRpxG88cYbw1133RW6d+8eZs2alfb37NkzdO3ateXtAKDDZseYMWPCqFGjwuabbx623HLLcPnll4f58+eHAw88MH1///33DwMGDAhjx45Nt88+++yw9dZbh7XWWit89NFH4aKLLgpvvfVWOPjgg1veZgBaTp8DABLFboA8VOVqt0KOB6BytHduXH311enr8OHDG+2//vrrwwEHHNDyhgDQYbNjn332Ce+//34444wzUsFi4403Dvfff3/o06dP+v706dNDp07/NyHghx9+GA455JB035VXXjmNDH/yySfDeuut1/JGA9Bi+hwAUEuxGwAAOsCUggCQVZyONm5NmThxYqPbl112WdoAqEz6HACUKsVugHyYxhyALOQGAFnJDgCykBsAkCh2A+ShqqZ2K+R4ACqH3AAgK9kBQBZyAwBqKXYD5MPVsgBkITcAyEp2AJCF3ACApFPtFwAAAAAAAAAoH0Z2A+QjXuxayAWvLpYFqCxyA4CsZAcAWcgNAEgUuwHyUJXLpa2Q4wGoHHIDgKxkBwBZyA0AqKXYDZAP6yABkIXcACAr2QFAFnIDABJrdgMAAAAAAABQdozsBshHvNi1psDjAagccgOArGQHAFnIDQBIFLsB8mAdJACykBsAZCU7AMhCbgBALdOYAwAAAAAAAFB2FLsB8hEvdo1XvLZ4y/6UV111VVhzzTVDly5dwlZbbRWefvrpZu87YcKEUFVV1WiLxwFQObkBQJmTHQBkITcAaE5VVbatzJnGHCAfdR2BQo7P4Oabbw5jxowJ48aNS4Xuyy+/PIwYMSJMmTIlrLbaak0e06NHj/T9OrHgDUBl5AYAHYDsACALuQFAc848c/F9l18ewpw5TX+vzCl2A+SjJlaPCzw+hDB37txGuzt37py2RV166aXhkEMOCQceeGC6HYve99xzT7juuuvCSSed1ORTxOJ23759C2gkAKWWGwBUENkBQBZyA4DmnHXW4vsmTKgtdjf1vTJnGnOAdjRo0KDQs2fP+m3s2LGL3WfhwoVh0qRJYaeddqrf16lTp3T7qaeeavax582bF9ZYY430HN/+9rfDSy+91GavAwAAAAAAoNgUuwHyUJXLFbxFM2bMCHPmzKnfTj755MWe64MPPgjV1dWhT58+jfbH27NmzWqyfUOGDEmjvu+6665www03hJqamjBs2LDw9ttvt9EZAaA9cgOAyiE7AMhCbtCUOIjmyCOPDG+99Vaxm9JhOcdtzzkmK8VugCzrIBWy/Xdd7YZbU1OYt8Q222wT9t9//7DxxhuH7bffPvzxj38Mq666arjmmmta5fEBKE5uAFBBZAcAWcgNmjB58uRw7bXXhu222y68/vrrxW5Oh+Qctz3nmKwUuwFKTO/evcMyyywTZs+e3Wh/vJ3vmtzLLbdc2GSTTcLUqVPbqJUAAAAAQCnZeuutw5///Ofw/vvvp0LhlClTit2kDsc5bnvOMVktm/kIgEpU6BWvGY5dfvnlw2abbRYefvjhMHLkyLQvTkseb8fpW/IRp0F/4YUXwm677dbiJgNQHrkBQAchOwDIQm5UnNNPPz18+OGHed136NCh4ZlnnkkzQD722GNpCUSWzjlue84xbUGxG6AEOxBjxowJo0aNCptvvnnYcsstw+WXXx7mz58fDjzwwPT9OGX5gAEDwtixY9Pts88+O13xttZaa4WPPvooXHTRRWlNk4MPPrjlbQag5XzwBEBWsgOALORGxbn++uvDO++8k+mYOFPkyy+/rEiYJ+e47TnHtAXFboB81IQQqgo8PoN99tknTdNyxhlnhFmzZqW1uO+///7Qp0+f9P3p06eHTp3+byWKeDXcIYccku678sorp5HhTz75ZFhvvfUKaDQA5ZIbAHQAsgOALORGxXn77bfzul8cMPPNb34zTJw4Mfz0pz8Ne+65Z5u3raNwjtuec0xbUOwGKFFxyvLmpi2PId/QZZddljYAAAAAoDJ9/PHHaVnDJ554Ipx//vnhxBNPLHaTOhznuO05x2Sl2A2Qh6pcLm2FHA9A5ZAbAGQlOwDIQm7QlKlTp4YXXnghXHrppeHYY48tdnM6JOe47TnHZKXYDZAP6yABkIXcACAr2QFAFnKDJmyyySapUNi7d+9iN6XDco7bnnNMVv+34CsAAAAAAABlS4Gw7TnHbc85JgsjuwHyUZOL8zsVdjwAlUNuAJCV7AAgC7kBQBZvvhk6KsVugHyYGgqALOQGAFnJDgCykBsAkCh2A+SlwA5EPB6ACiI3AMhKdgCQhdwAgMia3QAAAAAAAACUHSO7AfJhaigAspAbAGQlOwDIQm4AQKLYDZCPmtgByBV4PAAVQ24AkJXsACALuQEAiWnMAQAAAAAAACg7RnYD5CNXU7sVcjwAlUNuAJCV7AAgC7kBAIliN0A+rIMEQBZyA4CsZAcAWcgNAEgUuwHyYR0kALKQGwBkJTsAyEJuAED+xe7cf6/y6r3GSvncvaKtOnjlMHfu3DCgS5fweY/uxW5OyYvnKZ6vQct0DZ936VHs5pS0eI7iuVr1y6uEhdULit2cktZ7cK8Q/vF/v7tof3XnfpVBPYvdlLKw2qq1/77j14UL/S7M51wN6NolfN5Tzi5JPEdyI3+yo4Syo/8KxW5Kyes9oFv6991/hc7h85VWLHZzSlo8R/Fc1cwfEELN58VuTunrNCB0Wl525ENuFF/duR/QqUsIyxe7NaVtYKfav6G7LFg1dP98YbGbU/K6VK0qO/IlN/ImNwCgyMXujz/+OH294I+nt2FTOohJIZx98/HFbkVZubrYDSgj1xS7AeXiH//3u6tnz1YqtpoaKpO63Dj/ppOK3ZTy8NcQzv1ZsRtRPpyr/P2y2A2o5OyQGy3Pjl8dXeymlIWfXnVIsZtQNq78YbFbUG700PKiz1EyuXHdnn4f5mNcsRtQds4pdgPKiNzIi9wAgOIWu/v37x9mzJgRunfvHqqqqtquNQCtIF4lGzsP8XdX6z1ogZ2ACus/yA0gVHp2yI3MZAdQTvQ5ik9uAOVEbgBAkYvdnTp1CgMHDmzDZgC0rla7SraOq2UzkRtAqPTskBuZyQ6g3OhzFJfcAMqN3ACAttGpjR4XAAAAAAAAAIo7shug4tXUxP8r8HgAKobcACAr2QFAFnIDABLFboB8mBoKgCzkBgBZyQ4AspAbAJCYxhwAAAAAAACAsmNkN0A+XC0LQBZyA4CsZAcAWcgNAEgUuwHyURM7ALkCjwegYsgNALKSHQBkITcAIFHsBshDLleTtkKOB6ByyA0AspIdAGQhNwCgljW7AQAAAAAAACg7RnYD5LuOUSHTO1kHCaCyyA0AspIdAGQhNwAgUewGyLsDoAMBQJ7kBgBZyQ4AspAbAJCYxhwAAAAAAACAsmNkN0A+ampCqKpp+fG5Ao4FoPzIDQCykh0AZCE3ACBR7AbIh6mhAMhCbgCQlewAIAu5AQCJYjdAHnI1NSFXwNWyOVfLAlQUuQFAVrIDgCzkBgDUsmY3AAAAAAAAAGXHyG6AfJgaCoAs5AYAWckOALKQGwCQKHYD5KMmF0KVDgQAeZIbAGQlOwDIQm4AQGIacwAAAAAAAADKjpHdAHlf7VpT4PEAVAy5AUBWsgOALOQGACSK3QB5yNXkQq6AqaFyOhAAFUVuAJCV7AAgC7kBALUUuwHykasp8GrZAo4FoPzIDQCykh0AZCE3ACCxZjdAibrqqqvCmmuuGbp06RK22mqr8PTTTy/x/rfeemtYZ5110v032GCDcO+997ZbWwEoz+wAoLLpcwCQlT4HAKVGsRsg36mhCtyyuPnmm8OYMWPCmWeeGSZPnhw22mijMGLEiPDee+81ef8nn3wyfP/73w8HHXRQeO6558LIkSPT9uKLL7bSGQCglHOjJdkBQGnR5wAgC30OAKil2A2Q79ROhW4ZXHrppeGQQw4JBx54YFhvvfXCuHHjwgorrBCuu+66Ju9/xRVXhG984xvh+OOPD+uuu24455xzwqabbhquvPLKVjoBAJRybrQkOwAoMfocAGShzwEAiTW7AfLwRfg8hFyBx4cQ5s6d22h/586d09bQwoULw6RJk8LJJ59cv69Tp05hp512Ck899VSTjx/3xytrG4pX1t55550tbzQAZZEbLc0OAEqLPgcAWehztJ1crvbEPv/888VuSoc1ZcqU9DW+p+bNm1fs5nRIznHbc47bXt3v4brfy81R7AZYguWXXz707ds3PDGr8LXoVlxxxTBo0KBG++K0T2eddVajfR988EGorq4Offr0abQ/3n711VebfOxZs2Y1ef+4H4COnRstzQ4ASoM+BwBZ6HO07XrkcYuF/Wj06NHFblKHd+ihhxa7CR2ec9z2nOO29/HHH4eePXs2+33FboAl6NKlS5g2bVr9H7mFiFcfVVVVNdrX1JWyAJQvuQFAVrIDgCzkRts54ogj0lZTUxO+8pWvpNGai54fWs8WW2wRnnnmmWI3o0Nzjtuec9y2Yk5tttlmoX///ku8n2I3QB6diLi1l969e4dlllkmzJ49u9H+eDteuduUuD/L/QHoOLnR0uwAoHTocwC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      "text/plain": [
       "<Figure size 2000x400 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Q-value heatmaps and the learned policy side by side\n",
    "fig, axes = plt.subplots(1, len(action_space) + 1, figsize=(20, 4))  # Create subplots for heatmaps and policy\n",
    "\n",
    "# Plot Q-value heatmaps for each action\n",
    "plot_q_values_heatmap(sarsa_q_table, rows, cols, action_space, fig, axes[:-1])\n",
    "\n",
    "# Plot the learned policy\n",
    "plot_policy_grid(sarsa_q_table, rows, cols, terminal_states, axes[-1])\n",
    "\n",
    "# Adjust layout and display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of SARSA Q-Values and Policy**\n",
    "\n",
    "**Q-value Heatmaps (Left Four Plots)**  \n",
    "- These heatmaps show the learned Q-values for taking each specific action (up, down, left, right) in every state of the grid.  \n",
    "- **Brighter colors (yellow)** represent higher Q-values, indicating actions that are estimated to lead to better long-term rewards *under the current policy*.  \n",
    "- **Higher Q-values are observed for actions moving down and right**, reflecting the goal-oriented nature of the learned policy.  \n",
    "- The values propagate outward from the goal state (3,3), showing how rewards influence learning across the grid.  \n",
    "\n",
    "**Learned Policy (Rightmost Plot)**  \n",
    "- This grid shows the action with the highest Q-value for each state, representing the **greedy policy** derived from the learned Q-values. 'T' marks terminal states.  \n",
    "- **The arrows generally point downward (↓) and right (→), guiding the agent towards the goal at (3,3).**  \n",
    "- **Left (←) and up (↑) actions appear only in states where they have slightly higher Q-values due to exploration influence.**  \n",
    "- The **policy structure is stable**, reflecting the learned Q-values correctly.  \n",
    "\n",
    "**Overall Interpretation:**  \n",
    "- The SARSA agent has successfully **learned Q-values that reflect the expected return for following its (epsilon-greedy) policy**.  \n",
    "- **The derived greedy policy shows clear paths towards the goal state (3,3)**, following an efficient down-and-right trajectory.  \n",
    "- **Compared to Q-learning**, which updates based on the highest expected reward, SARSA's policy is **more influenced by exploration and learned experience**. In this case, however, the final policy remains nearly identical.  \n",
    "- **The learning process has converged**, as the Q-values and policy remain stable.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Q-Values and Optimal Policy\n",
    "Let's examine the optimal policy learned by SARSA in a tabular format, showing the Q-values for each action in each state and the corresponding best action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State      up         down       left       right      Optimal Action \n",
      "-----------------------------------------------------------------\n",
      "(0, 0)     0.00       0.00       0.00       0.00       Terminal       \n",
      "(0, 1)     0.00       0.07       0.99       0.50       left           \n",
      "(0, 2)     0.84       5.49       0.17       0.00       down           \n",
      "(0, 3)     0.13       6.66       0.51       0.67       down           \n",
      "(1, 0)     1.00       0.56       0.22       0.16       up             \n",
      "(1, 1)     0.14       0.57       0.81       0.01       left           \n",
      "(1, 2)     0.55       7.15       0.07       2.17       down           \n",
      "(1, 3)     0.06       8.52       1.80       1.57       down           \n",
      "(2, 0)     0.72       0.40       0.05       4.20       right          \n",
      "(2, 1)     0.07       0.36       0.32       7.28       right          \n",
      "(2, 2)     0.43       3.24       0.70       8.85       right          \n",
      "(2, 3)     3.62       10.00      2.20       6.68       down           \n",
      "(3, 0)     0.07       0.46       0.27       5.96       right          \n",
      "(3, 1)     1.68       2.00       1.05       8.31       right          \n",
      "(3, 2)     2.07       4.49       2.52       10.00      right          \n",
      "(3, 3)     0.00       0.00       0.00       0.00       Terminal       \n"
     ]
    }
   ],
   "source": [
    "# Create a list of dictionaries to represent the SARSA Q-table data and derived policy\n",
    "sarsa_q_policy_data = []\n",
    "for r in range(rows):\n",
    "    for c in range(cols):\n",
    "        state = (r, c)  # Define the current state as a tuple (row, col)\n",
    "        if state in sarsa_q_table:  # Check if the state exists in the SARSA Q-table\n",
    "            actions = sarsa_q_table[state]  # Retrieve the Q-values for all actions in the current state\n",
    "            if actions:  # Check if the action dictionary is not empty\n",
    "                # Determine the best action based on the highest Q-value, or mark as 'Terminal' for terminal states\n",
    "                best_action = max(actions, key=actions.get) if state not in terminal_states else 'Terminal'\n",
    "                # Append the state, Q-values, and optimal action to the policy data list\n",
    "                sarsa_q_policy_data.append({\n",
    "                    'State': state,\n",
    "                    'up': actions.get('up', 0.0),  # Use .get to safely retrieve Q-value for 'up', default to 0.0 if missing\n",
    "                    'down': actions.get('down', 0.0),  # Retrieve Q-value for 'down'\n",
    "                    'left': actions.get('left', 0.0),  # Retrieve Q-value for 'left'\n",
    "                    'right': actions.get('right', 0.0),  # Retrieve Q-value for 'right'\n",
    "                    'Optimal Action': best_action  # Store the optimal action for the current state\n",
    "                })\n",
    "            else:  # Handle cases where the action dictionary for a state is empty\n",
    "                sarsa_q_policy_data.append({\n",
    "                    'State': state,\n",
    "                    'up': 0.0, 'down': 0.0, 'left': 0.0, 'right': 0.0,  # Default Q-values to 0.0\n",
    "                    'Optimal Action': 'N/A'  # Mark the optimal action as 'N/A'\n",
    "                })\n",
    "        else:  # Handle cases where the state is not present in the Q-table (shouldn't happen with full initialization)\n",
    "            sarsa_q_policy_data.append({\n",
    "                'State': state,\n",
    "                'up': 0.0, 'down': 0.0, 'left': 0.0, 'right': 0.0,  # Default Q-values to 0.0\n",
    "                'Optimal Action': 'N/A'  # Mark the optimal action as 'N/A'\n",
    "            })\n",
    "\n",
    "# Sort the policy data by state for better readability (optional)\n",
    "sarsa_q_policy_data.sort(key=lambda x: x['State'])\n",
    "\n",
    "# Display the Q-table data in a tabular format\n",
    "header = ['State', 'up', 'down', 'left', 'right', 'Optimal Action']  # Define the table headers\n",
    "print(f\"{header[0]:<10} {header[1]:<10} {header[2]:<10} {header[3]:<10} {header[4]:<10} {header[5]:<15}\")  # Print the header row\n",
    "print(\"-\" * 65)  # Print a separator line\n",
    "\n",
    "# Iterate through the policy data and print each row in a formatted manner\n",
    "for row_data in sarsa_q_policy_data:\n",
    "    print(f\"{row_data['State']!s:<10} {row_data['up']:<10.2f} {row_data['down']:<10.2f} {row_data['left']:<10.2f} {row_data['right']:<10.2f} {row_data['Optimal Action']:<15}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis of Tabular SARSA Q-Values and Policy**  \n",
    "\n",
    "This table presents the learned Q-values for each state-action pair and the optimal action determined by selecting the highest Q-value at each state.  \n",
    "\n",
    "**Key Observations:**  \n",
    "\n",
    "1. **Q-Value Trends and Value Propagation:**  \n",
    "   - States **closer to the goal (3,3) have higher Q-values**, as they represent better long-term reward potential.  \n",
    "   - For example, **state (3,2) has a high Q-value for moving right (10.00)**, confirming that it leads directly to the goal.  \n",
    "\n",
    "2. **Optimal Action Selection:**  \n",
    "   - The **Optimal Action** column confirms that the learned policy follows a logical path toward the goal, primarily favoring **right (→) and down (↓) movements**.  \n",
    "   - In some cases, alternative actions (e.g., up or left) have slightly lower Q-values, indicating **exploration-driven adjustments** during training.  \n",
    "\n",
    "3. **Terminal States:**  \n",
    "   - As expected, the **starting state (0,0) and goal state (3,3) are terminal**, meaning no further actions are taken.  \n",
    "   - Their Q-values remain **zero**, as they do not contribute to further learning.  \n",
    "\n",
    "4. **Cliff Avoidance and Risk-Aware Behavior:**  \n",
    "   - The **bottom row (near the cliff) has more varied Q-values**, showing that SARSA has learned to balance between optimality and safety.  \n",
    "   - Some states near the cliff show **non-greedy actions** (e.g., moving left or up) to avoid risky areas.  \n",
    "   - This aligns with SARSA's **on-policy nature**, which often results in a more conservative strategy compared to Q-learning.  \n",
    "\n",
    "5. **Consistency with Visualizations:**  \n",
    "   - The learned **policy matches the arrow grid visualization**, confirming that the agent follows the expected trajectory.  \n",
    "   - Any slight discrepancies may be due to **tie-breaking in Q-values**, where multiple actions have similar expected returns.  \n",
    "\n",
    "**Comparison to Q-Learning (Implicit Differences):**  \n",
    "- **SARSA’s on-policy updates** tend to prioritize safer paths, which can be seen in cases where it avoids risky moves near the cliff.  \n",
    "- In contrast, **Q-learning (off-policy) might have resulted in a more aggressive policy**, optimizing for maximum reward while risking falls more frequently.  \n",
    "- The difference is most noticeable in **cliff-adjacent states**, where SARSA has likely learned a more **risk-averse approach**.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing with Different Hyperparameters (Optional)\n",
    "\n",
    "Experiment with different values for the learning rate (α), discount factor (γ), and initial epsilon (ε₀) to observe their impact on SARSA's learning speed and convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running SARSA with different hyperparameters...\n",
      "  Training with alpha=0.1, gamma=0.9, epsilon_init=1.0\n",
      "  Training with alpha=0.1, gamma=0.99, epsilon_init=1.0\n",
      "  Training with alpha=0.5, gamma=0.9, epsilon_init=1.0\n",
      "  Training with alpha=0.5, gamma=0.99, epsilon_init=1.0\n",
      "Experiments finished.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Experiment with different hyperparameters for SARSA\n",
    "learning_rates = [0.1, 0.5]        # Test different learning rates (alpha = 0.1 and 0.5)\n",
    "discount_factors = [0.9, 0.99]     # Test different discount factors (gamma = 0.9 and 0.99)\n",
    "exploration_rates = [1.0]          # Keep initial exploration rate (epsilon) at 1.0 for these tests\n",
    "min_epsilon = 0.1                  # Minimum exploration rate (epsilon)\n",
    "decay_rate = 0.01                  # Epsilon decay rate\n",
    "episodes = 500                     # Number of episodes to train\n",
    "max_steps = 100                    # Maximum steps per episode\n",
    "\n",
    "# Store results for comparison\n",
    "sarsa_results = []\n",
    "\n",
    "# Define environment parameters again\n",
    "rows, cols = 4, 4                  # Grid dimensions (4x4)\n",
    "terminal_states = [(0, 0), (3, 3)] # Terminal states in the grid\n",
    "rewards = {(0, 0): 1, (3, 3): 10}  # Rewards for terminal states\n",
    "grid, state_space, action_space = create_gridworld(rows, cols, terminal_states, rewards)\n",
    "\n",
    "# Run experiments with different hyperparameter combinations\n",
    "print(\"Running SARSA with different hyperparameters...\")\n",
    "for alpha in learning_rates:  # Loop through learning rates\n",
    "    for gamma in discount_factors:  # Loop through discount factors\n",
    "        for initial_epsilon in exploration_rates:  # Loop through initial exploration rates\n",
    "            print(f\"  Training with alpha={alpha}, gamma={gamma}, epsilon_init={initial_epsilon}\")\n",
    "            \n",
    "            # Run SARSA with the current hyperparameter set\n",
    "            q_table, rewards_per_episode, episode_lengths = run_sarsa(\n",
    "                state_space, action_space, rewards, terminal_states, rows, cols, alpha, gamma,\n",
    "                initial_epsilon, min_epsilon, decay_rate, episodes, max_steps\n",
    "            )\n",
    "\n",
    "            # Store the results for later analysis\n",
    "            sarsa_results.append({\n",
    "                'alpha': alpha,                          # Learning rate\n",
    "                'gamma': gamma,                          # Discount factor\n",
    "                'initial_epsilon': initial_epsilon,      # Initial exploration rate\n",
    "                'rewards_per_episode': rewards_per_episode,  # Rewards per episode\n",
    "                'episode_lengths': episode_lengths       # Episode lengths\n",
    "            })\n",
    "print(\"Experiments finished.\")\n",
    "\n",
    "# --- Visualization of Hyperparameter Effects ---\n",
    "num_results = len(sarsa_results)  # Total number of hyperparameter combinations tested\n",
    "# Determine grid size for subplots (e.g., 2x2 for 4 results)\n",
    "plot_rows = int(np.ceil(np.sqrt(num_results)))  # Number of rows in the subplot grid\n",
    "plot_cols = int(np.ceil(num_results / plot_rows))  # Number of columns in the subplot grid\n",
    "\n",
    "# Create a figure for the plots\n",
    "plt.figure(figsize=(6 * plot_cols, 4 * plot_rows))  # Adjust figure size based on grid dimensions\n",
    "\n",
    "# Create a larger figure to visualize all hyperparameter combinations\n",
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "# Plot the rewards per episode for each hyperparameter combination\n",
    "for i, result in enumerate(sarsa_results):\n",
    "    plt.subplot(plot_rows, plot_cols, i + 1)  # Create a subplot for each result\n",
    "    plt.plot(result['rewards_per_episode'])  # Plot the rewards per episode\n",
    "    plt.title(f\"SARSA: α={result['alpha']}, γ={result['gamma']}, ε₀={result['initial_epsilon']}\")  # Title with hyperparameters\n",
    "    plt.xlabel('Episode')  # Label for the x-axis\n",
    "    plt.ylabel('Total Reward')  # Label for the y-axis\n",
    "    plt.grid(True)  # Add grid for better readability\n",
    "    # Set consistent Y-axis limits across all plots for better comparison\n",
    "    plt.ylim(\n",
    "        min(min(r['rewards_per_episode']) for r in sarsa_results) - 1, \n",
    "        max(max(r['rewards_per_episode']) for r in sarsa_results) + 1\n",
    "    )\n",
    "\n",
    "# Add a super title for the entire figure\n",
    "plt.suptitle(\"SARSA Performance with Different Hyperparameters\", fontsize=16, y=1.02)\n",
    "plt.tight_layout()  # Adjust layout to prevent overlap\n",
    "plt.show()  # Display the plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying SARSA to Different Environments (Cliff Walking)\n",
    "In this section, we will apply the SARSA algorithm to a different environment: Cliff Walking. This environment is a classic reinforcement learning problem where the agent must navigate a grid while avoiding cliffs (negative rewards) to reach a goal state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Cliff Walking environment parameters\n",
    "cliff_rows, cliff_cols = 4, 12\n",
    "cliff_start_state = (3, 0)\n",
    "cliff_terminal_state = (3, 11)\n",
    "cliff_states = [(3, c) for c in range(1, 11)]\n",
    "\n",
    "# Define rewards: -1 for normal steps, -100 for cliff, +10 for goal (implicit goal reward handled by update)\n",
    "# SARSA/Q-learning typically uses reward for *transitioning* into a state.\n",
    "# We can simulate the standard Cliff Walking rewards: -1 per step, -100 for falling.\n",
    "# Let's adapt the reward/transition logic slightly for this common setup.\n",
    "\n",
    "# Modified State Transition and Reward for standard Cliff Walking\n",
    "def cliff_state_transition_reward(\n",
    "    state: Tuple[int, int],\n",
    "    action: str,\n",
    "    rows: int,\n",
    "    cols: int,\n",
    "    cliff_states: List[Tuple[int, int]],\n",
    "    start_state: Tuple[int, int]\n",
    ") -> Tuple[Tuple[int, int], int]:\n",
    "    \"\"\"\n",
    "    Compute the next state and reward for Cliff Walking.\n",
    "    Reward is -1 for normal steps, -100 for falling into cliff.\n",
    "    Falling into cliff resets state to start_state.\n",
    "    \"\"\"\n",
    "    row, col = state\n",
    "    next_row, next_col = row, col\n",
    "\n",
    "    # Calculate potential next position\n",
    "    if action == 'up' and row > 0:\n",
    "        next_row -= 1\n",
    "    elif action == 'down' and row < rows - 1:\n",
    "        next_row += 1\n",
    "    elif action == 'left' and col > 0:\n",
    "        next_col -= 1\n",
    "    elif action == 'right' and col < cols - 1:\n",
    "        next_col += 1\n",
    "\n",
    "    next_state = (next_row, next_col)\n",
    "\n",
    "    # Determine reward\n",
    "    if next_state in cliff_states:\n",
    "        reward = -100\n",
    "        next_state = start_state # Reset to start if falls into cliff\n",
    "    elif next_state == cliff_terminal_state:\n",
    "        reward = 0 # Standard setup often gives 0 for reaching goal, -1 transition cost handled step reward\n",
    "                   # Alternative: give +10 or other positive reward here if desired. Let's stick to -1 step cost.\n",
    "        reward = -1 # Apply step cost even for reaching goal for consistency? No, goal transition is special.\n",
    "                    # Let's use the common -1 step cost structure. Goal state itself gives no reward/penalty on arrival.\n",
    "        reward = -1\n",
    "    else:\n",
    "        reward = -1 # Standard step cost\n",
    "\n",
    "    return next_state, reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modified SARSA episode runner for Cliff Walking reward structure\n",
    "def run_sarsa_cliff_episode(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]],\n",
    "    action_space: List[str],\n",
    "    terminal_state: Tuple[int, int],\n",
    "    cliff_states: List[Tuple[int, int]],\n",
    "    start_state: Tuple[int, int],\n",
    "    rows: int,\n",
    "    cols: int,\n",
    "    alpha: float,\n",
    "    gamma: float,\n",
    "    epsilon: float,\n",
    "    max_steps: int\n",
    ") -> Tuple[int, int]:\n",
    "    \"\"\" Runs a single episode of SARSA for Cliff Walking. \"\"\"\n",
    "    state = start_state\n",
    "    action = epsilon_greedy_policy(state, q_table, action_space, epsilon)\n",
    "    total_reward = 0\n",
    "    steps = 0\n",
    "\n",
    "    for _ in range(max_steps):\n",
    "        next_state, reward = cliff_state_transition_reward(\n",
    "            state, action, rows, cols, cliff_states, start_state\n",
    "        )\n",
    "        total_reward += reward\n",
    "\n",
    "        next_action = epsilon_greedy_policy(next_state, q_table, action_space, epsilon)\n",
    "\n",
    "        # Update Q-value for (s, a)\n",
    "        # Special handling for terminal state: Q(terminal, any_action) = 0\n",
    "        if next_state == terminal_state:\n",
    "             q_next = 0.0\n",
    "        else:\n",
    "             # Check bounds and existence before accessing q_table\n",
    "             if next_state in q_table and next_action in q_table[next_state]:\n",
    "                 q_next = q_table[next_state][next_action]\n",
    "             else: # Should not happen with full init, but safe check\n",
    "                 q_next = 0.0\n",
    "\n",
    "        td_target = reward + gamma * q_next\n",
    "        td_error = td_target - (q_table[state][action] if state in q_table and action in q_table[state] else 0.0)\n",
    "        if state in q_table and action in q_table[state]: # Check if state-action exists before updating\n",
    "             q_table[state][action] += alpha * td_error\n",
    "\n",
    "        state = next_state\n",
    "        action = next_action\n",
    "        steps += 1\n",
    "\n",
    "        if state == terminal_state:\n",
    "            break\n",
    "\n",
    "    return total_reward, steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modified SARSA runner function for Cliff Walking\n",
    "def run_sarsa_cliff(\n",
    "    action_space: List[str],\n",
    "    terminal_state: Tuple[int, int],\n",
    "    cliff_states: List[Tuple[int, int]],\n",
    "    start_state: Tuple[int, int],\n",
    "    rows: int,\n",
    "    cols: int,\n",
    "    alpha: float,\n",
    "    gamma: float,\n",
    "    initial_epsilon: float,\n",
    "    min_epsilon: float,\n",
    "    decay_rate: float,\n",
    "    episodes: int,\n",
    "    max_steps: int\n",
    ") -> Tuple[Dict[Tuple[int, int], Dict[str, float]], List[int], List[int]]:\n",
    "    \"\"\" Executes SARSA for Cliff Walking. \"\"\"\n",
    "    # Create state space dynamically or pass it - here, generating inside\n",
    "    state_space = [(r, c) for r in range(rows) for c in range(cols)]\n",
    "    q_table = initialize_q_table_nested(state_space, action_space)\n",
    "\n",
    "    rewards_per_episode = []\n",
    "    episode_lengths = []\n",
    "\n",
    "    for episode in range(episodes):\n",
    "        epsilon = adjust_epsilon(initial_epsilon, min_epsilon, decay_rate, episode)\n",
    "        total_reward, steps = run_sarsa_cliff_episode(\n",
    "            q_table, action_space, terminal_state, cliff_states, start_state,\n",
    "            rows, cols, alpha, gamma, epsilon, max_steps\n",
    "        )\n",
    "        rewards_per_episode.append(total_reward)\n",
    "        episode_lengths.append(steps)\n",
    "        # Optional: Print progress\n",
    "        # if (episode + 1) % 100 == 0:\n",
    "        #     print(f\"Episode {episode + 1}/{episodes} completed. Avg Reward (last 100): {np.mean(rewards_per_episode[-100:]):.2f}\")\n",
    "\n",
    "\n",
    "    return q_table, rewards_per_episode, episode_lengths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run SARSA for Cliff Walking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running SARSA on Cliff Walking environment...\n",
      "SARSA training on Cliff Walking finished.\n"
     ]
    }
   ],
   "source": [
    "# --- Run SARSA on Cliff Walking ---\n",
    "# Hyperparameters (can be tuned)\n",
    "alpha_cliff = 0.1         # Learning rate\n",
    "gamma_cliff = 0.99        # Discount factor (higher gamma encourages looking further ahead)\n",
    "initial_epsilon_cliff = 0.2 # Start with less exploration than 1.0? Let's try 0.2 initially, then maybe 1.0\n",
    "min_epsilon_cliff = 0.01   # Lower minimum epsilon\n",
    "decay_rate_cliff = 0.005   # Slower decay?\n",
    "episodes_cliff = 500       # Number of episodes\n",
    "max_steps_cliff = 200      # Max steps per episode\n",
    "\n",
    "print(\"Running SARSA on Cliff Walking environment...\")\n",
    "# Define action space\n",
    "cliff_action_space = ['up', 'down', 'left', 'right']\n",
    "\n",
    "# Run the training\n",
    "cliff_q_table_sarsa, cliff_rewards_sarsa, cliff_lengths_sarsa = run_sarsa_cliff(\n",
    "    cliff_action_space, cliff_terminal_state, cliff_states, cliff_start_state,\n",
    "    cliff_rows, cliff_cols, alpha_cliff, gamma_cliff,\n",
    "    initial_epsilon_cliff, min_epsilon_cliff, decay_rate_cliff, episodes_cliff, max_steps_cliff\n",
    ")\n",
    "print(\"SARSA training on Cliff Walking finished.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot the rewards, we can use the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot rewards for the Cliff Walking environment\n",
    "def plot_rewards(rewards_per_episode: List[int], ax: plt.Axes = None) -> plt.Axes:\n",
    "    \"\"\"\n",
    "    Plot the total rewards accumulated over episodes.\n",
    "\n",
    "    Parameters:\n",
    "    - rewards_per_episode (List[int]): List of total rewards per episode.\n",
    "    - ax (plt.Axes, optional): Matplotlib axis to plot on. If None, a new figure and axis are created.\n",
    "\n",
    "    Returns:\n",
    "    - plt.Axes: The Matplotlib axis containing the plot.\n",
    "    \"\"\"\n",
    "    # If no axis is provided, create a new figure and axis\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    \n",
    "    # Plot the rewards over episodes\n",
    "    ax.plot(rewards_per_episode)\n",
    "    ax.set_xlabel('Episode')  # Label for the x-axis\n",
    "    ax.set_ylabel('Total Reward')  # Label for the y-axis\n",
    "    ax.set_title('Rewards Over Episodes')  # Title of the plot\n",
    "    \n",
    "    # Return the axis for further customization if needed\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the results, we can plot the rewards and episode lengths similar to before. Let's also visualize the learned policy for the Cliff Walking environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CC3+33HJLfv7en/70pxg0aFB+xsRNN93Ua5//5z//efL2ihUr4sEHH4xrrrkmf3UrVNMTTzwRjzzySFx66aVx6qmnRrOTR5ksUvJIyaNMFil5pORRJouUPMqaNYvrr78+L3hOnDgxRo8enT92wAEHREdHR9x22219uuiXUfgDAAASpVJL3iq9tyg33HBDnHjiiXHBBRfkr7Ts7OyMRx99tFf7OPzww9d47GMf+1i8+93vjuuuuy5OOOGEXu0P1maXXXbJn2wZNWpUrYfSJ8ijTBYpeaTkUSaLlDxS8iiTRUoeZc2axfDhw/MVfx/+8Idj2rRp+WPZ75933HFH7L777tHXKfwBAAB9XvZL1imnnBLf/va3k+Lbu971rqr0v+eee8ZJJ51Ulb7gbzXbkyxvRx5lskjJIyWPMlmk5JGSR5ksUvIoa9Yshg4dmu84k704NDvz7/bbb88LofVA4a8XLF++PG+rLFq0KL+O326TGDhgcKF9j9lqVHItWucGg6rSz9gtNkquRVs+YkBV+hm3+cjkWrTl7a1V6WfLTUcm12roaC9uNcHf2mr0hsm1aCuGVedgpPEbjUyuResc1l2VfrbecGRyLVrL8BVV6Wfr9o2Sa9GGD19WlX62HDoquRZtk6HDq9LPmMGbJNdq2HzwkML7GN22eXKthpFtxc+rfcC45Fqk1zo6IuL+qBs9/Cdp1c/Eq7S1teWtUtkvWs8991x+Bl/2y9a8efNi5513zguBO+64YxQpO8Phu9/9bmy+efW+/gEAAKitwYMH51t71huFv14wZcqUNz3v45JrTor29vaqjOH0iz4ejeiMMz8ajeicr3woGtEFJx4ajerbn2zMuV320cac13c/2Jh/xy7/QGN+T5yya2P+G/bVHT4TjehT406JRrTPO84tvI+VhbD/jGbZ6vONZx5885vfjHPOOafiMc2ePTu/Zp8jO19iyy23jH/+53+O/fffPx5//PEYObJ3XvSx4YYbRktLee6lUik/0H7IkCFx7bXX9kofAAAAUBSFv14wadKkOO2005IndbInOr563JVVWfGXFf0u/Pp18exTL0cjrfjLin7nn3dTzJ3zl4Za8ZcV/c75zi/imecWNNSKv6zo941/+2U8Pa/4eVV7xV9W9Pvaf/wynnrplYZa8ZcV/U696Zcx+y8LGmrFX1b0+/Jtv4gnX1nQUCv+sqLfKb+/KZ5c9JeGWvGXFf0mPXBdPP1q8f+GbTJ0cVRDttIvK/pdMvPqeHbp/Kr0ueWw4r/es5V+WdHv/z5zeby0/LmohncOKj6/bKVfVvT73Qtnx6IVzxTa12uLsxV/dSL756jSf5Jev2/u3LnJi+DearXf6aefHhdddNFaP+XMmTOju3vlvyVnnHFG/MM//EP+56uvvjrGjBkTP/3pT+Ozn/1s9IbLLrssKfxlKwyzw9z32GOPvCgIAAAAfZnCXy94q22LZs+aH/37Vb6d0frIin5PzHyh8H5WjCp+y62/lRX9/vzn4p/0W7bRwKimrOj3+FMvFt7P0g2rU/hbJSv6PTan+Hlllm9YncLfKlnRb+Zzxc+tY4PqFP5WyYp+M+YXP68Vy6pT+FslK/pNf7n4ebV0VvdJ/KzoN/2V4r8nblB6NaopK/o9tuj5wvt5NRZGNWVFv9mvPluVvgb0q06BMZMV/Z5b+lRV+tqoZW5US1b0W7B8VqF9LO3ojGaSFf3WZfeLiRMnxoQJE9b6MePHj48XXnhhjTP9sp/Bs/fNmTMnesvbjQUAAAD6MoU/AADgDbIX2FT6Ipv1uy9bTZe1t7Pbbrvlhb5Zs2bFBz7wgfyxFStWxNNPPx3jxvXsjMY//elP6/yx733ve3vUFwAAABRJ4Q8AAOj1rT57W7Z68HOf+1x+VmC2rX5W7Pv2t7+dv++oo47q0efeeeed8+09s/P81ib7mK6urh71BQAAAEVS+AMAAPp84S+TFfr69+8fxx57bCxdujQ/d+/OO+/s8dl7Tz1Vne1rAQAAoGgKfwAAQF0YMGBAXHLJJXnrTT3dKhQAAAD6CoU/AAAgVWpZ2Sq9twHMmDEj5syZEx0dHcnjH/nIR2o2JgAAAHg7Cn8AAEAiO+rubY67W+u99Wz27NlxxBFHxCOPPJKc+5f9OeOMPwAAAPqy1loPAAAA6KNn/FXa6tgpp5wSW221Vbz44osxZMiQmD59evz2t7+N3XffPX7zm9/UengAAACwVlb8AQAAvO6ee+6JO++8M0aNGhWtra15+8AHPhBTpkyJL3/5y/Hggw/WeogAAADwlqz4AwAA3vyMv0pbHcu28hw+fHj+56z49/zzz+d/HjduXMyaNavGowMAAIC1s+IPAABItJRWtkrvrWc77rhjPPzww/l2n3vssUdcfPHFMXDgwLjyyitj/PjxtR4eAAAArJXCHwAAkOrJWX11Xvg788wz49VXX83/fO6558aHPvSh2GeffWKjjTaK6667rtbDAwAAgLVS+AMAAJre7rvvHv/4j/8Yn/zkJ6O9vT1/bJtttonHHnssFixYEBtuuGG0tNT3NqYAAAA0Pmf8AQAA0exn/O20007xT//0T/GOd7wjPv3pT8dvfvOb1e8bOXKkoh8AAAB1QeEPAAB4860+K2116Kqrrop58+bFFVdcEXPmzImDDjooX/F3wQUXxHPPPVfr4QEAAMA6UfgDAACi2Qt/mSFDhsSECRPy1X6PP/54fOITn4gf/OAHseWWW8Zhhx0WN954Y62HCAAAAGul8AcAAPAGW2+9dZx33nnx9NNPx09+8pP4f//v/8VRRx1V62EBAADAWvVf+7sBAICm05OVe3W84u+NspV/V199ddxwww3Rv3//OPHEE2s9JAAAAFgrhT8AACBValnZKr23jj377LMxderUvM2ePTv22Wef+N73vpev9hs8eHCthwcAAABrpfAHAAAkWkorW6X31qPrr78+fvSjH8Wvf/3r2HjjjeO4446L448/PrbZZptaDw0AAADWmcIfAADQ9I455pg47LDD4uc//3kceuih0drqOHQAAADqj8IfAAAQzX7GX7bFZ7bS729lhcAf/vCH8Y53vKNm4wIAAID14WWsAABA03tj0S/z29/+NpYuXVqT8QAAAEAlrPgDAAASLT04qy+7FwAAAKgNhb8ivfRKROvAYvvYZOjK64JFES8uKLaviOgcM7zwPvJ+BvZbfe0ctPLPRWrpqs6eVC3dpdXXavQ5cEl15jXgtdLqa7X6bO2qSjfRNmTlfNpeKcXgl4ufW8eIqL4q/CdrXVadBeatHa2rr9Xos2vJgKiG0qCV/1yXlvaP7ir0+Y6xi6MaRg19dfX1HaXq9NmodhnyTOF9bNg2KL9uN+iF2Lil+P4y72l7vvA+Bg3YIL9uO2B+LItnC+1rSUd3oZ+f3jdu3LgYMKA63+vpO2bNmhXDhg2LZjdz5szk2uzkUSaLlDxS8iiTRUoeZbJIySMljzJZrPl7yrpS+AMAAFKllpWt0nsbxKOPPlrrIVAFV1xxRd66ula+suykk06q9ZD6lGOOOabWQ+hT5FEmi5Q8UvIok0VKHmWySMkjJY8yWaw/hT8AAGDN1eCVrgivzuL/Qv3pT3+Kxx9/PAYOHBjbbrttbL/99rUeEgU6+eST87Zo0aIYMWJEXHnllbHbbrtFs8teWZ09yXLttdfGDjvsEM1OHmWySMkjJY8yWaTkUSaLlDxS8iiTRer+++9f5xcpKvwBAACpJi383XvvvXHCCSfEjBkzolR6fYv2lpZ43/veF9dcc01st912+WMLFiyIkSNH1ni0FCX777zrrrvWehh9RvYkizzK5FEmi5Q8UvIok0VKHmWySMkjJY8yWay0ZMmSWFfVOXQJAACgD8uKfQcddFAMHjw4f0XpAw88kLd///d/z7eA3GuvveL555+P733ve3kDAACAvsiKPwAAINFSWtkqvbcenXPOOXHwwQfHDTfckK/yW2XnnXeOo48+Oo488sg44IADYu7cufE///M/NR0rAAAAvBWFPwAAIJp9q89p06blBb2/Lfqtkj32jW98I/bYY4/8Y/bbb7+ajBEAAADejsIfAAAQzV74W7x4cWyyySZv+f5NN900BgwYEIccckhVxwUAAADrwxl/AABA0xs3blzce++9b/n+P/7xj/nHAAAAQF+m8AcAALzpGX+Vtnr0iU98Ik477bR49NFH13jfI488El/96lfzjwEAAIC+zFafAABAqtSyslV6bx2aNGlS3HHHHbHzzjvHwQcfHDvssEOUSqWYOXNm/vj73//+/GMAAACgL1P4AwAAotnP+Bs0aFBMmzYtLrvssvjJT34Sd911V/74O9/5zjjvvPPi1FNPjba2tloPEwAAANZK4Q8AACAiBg4cGF//+tfzBgAAAPVI4Q8AAEj05Ky+ej3jDwAAABqBwh8AABDNvtUnAAAANAKFPwAAINWDFX8KfwAAAFA7rTXsGwAAAAAAAOglCn8AAMCbb/VZaatjy5Yte8v3vfDCC1UdCwAAAKwvhT8AAKAuCn+PP/54HH744TFq1Khob2+PD3zgAzFt2rRe7WPXXXeNhx56aI3Hb7jhhnjve9/bq30BAABAb1P4AwAAEtn5fj1pRfnQhz4UnZ2dceedd8b9998fO+20U/7YvHnzeq2P/fffP/bcc8+46KKL8rdfffXVmDBhQhx77LHxjW98o9f6AQAAgCL0L+SzAgAATW3RokXJ221tbXmr1Msvvxx//vOf46qrrlq98u7CCy+M733ve/Hoo4/GpptuGr0h+3yHHXZY/OM//mP84he/yLf3HDZsWNx7772x44479kofAAAAUBQr/gAAgF43duzYGDFixOo2ZcqUHn2+jTbaKLbbbrv48Y9/nK/Cy1b+/eAHP4iNN944dtttt+hNf//3fx9HHnlk/OEPf4g5c+bkq/8U/QAAAKgHVvwBAACpnpzV9/p9c+fOzc/hW6Unq/0yLS0tcccdd8RHP/rRGD58eLS2tuZFv1tvvTU23HDD6C1PPvlkfPKTn8y3D/3Vr34Vd911V3zkIx+JU045Jc4///wYMGBAr/UFAAAAvc2KPwAAoNfP+MuKfn/b3qrwd/rpp+dFvbW1xx57LEqlUpx88sl5se93v/tdvvVmVgT88Ic/nG/H2Vt23nnn2GqrreLhhx+Ogw8+OM4777yYNm1a3HjjjfH+97+/1/oBAACAIljxBwAA1MzEiRNjwoQJa/2Y8ePHx5133pmfuffKK6+sXkmYncd3++23xzXXXJMXEHtD9jmPPfbY5LG99947HnzwwfjKV77SK30AAABAURT+AACANVW61ed6Gj16dN7ezmuvvZZfsy0+/1b2dnd3d6+N541Fv1Wy7UWvuuqqXusHAAAAiqDwBwAA9PoZf71tr732ys/yO+644+Lss8+OwYMHx7/927/FU089FYcddliv9zdjxoyYM2dOdHR0rH4s23Y021oUAAAA+iqFv16wfPnyvK2yaNGi/Dr+3ZvHwP6DCu17zDabJNeiLdu6Ov1sMWZkci1ad1tLVfrZYvORybVoXQOrc4znuM1GJtdq6KzSf7Mt3zEyuRZtyaZV6SbGbzQyuRate2BVuomtR45MrkXrHtJVlX623mBkci3a+GHVWeYzZvAmybVo/aL3VgT1pXllNmxbWHgf7QPGJddqGDSg+K/5tv7bJNcidQ7ojIjeO4uuSH97Vl8l9xZh1KhRceutt8YZZ5wRBx54YKxYsSLe/e53x3/913/FTjvt1Gv9zJ49O4444oh45JFH8kJfdrZgJvtzpqurOt/7aU6dnZ15cfv++++P//7v/45tt9221kOCPsnflZQ8ymTBW/G1AVTqlFNOieuuuy7mzZsX9ULhrxdMmTIlJk+evMbjl/z3xNXnjxTt9O8fH43o7NM/Eo3onNMa85Xi536p919t31ec/9lDoxFd9tHGnNd3DmvMeX33oMb83vH1HY6LRvTVHT4Tjej/e8d50Yi2GP0vhfexaGj24rARhffTyHbffff41a9+VfgvdVtttVX8+te/zq/33ntv/OUvf8nPIrzkkksK7Zvmlq0u/fjHPx433XRT/vb++++fn225/fbb13po0Kf4u5KSR5kseCu+NoCeWLhwYcyfPz/qicJfL5g0aVKcdtppyYq/sWPHxlc/8s9VWfGXFf0u/NyP4tkniv/iW7b9O6IaspV+WdHv3Av/O+Y8u6ChVvxlRb9zLr055jy3oKFW/GVFv7P/zy3xzPPFz6vaK/6yot8ZP/hlPP1C8XNbMjaqIlvplxX9Tr3plzH7LwsaasVfVvT7yi2/jCcXLGioFX9Z0e/Lv745nvxr8fMav1V1fpjJVsRlRb+LZl4Tzy6d31Ar/rKi3yUzr67KvDKHjJpeeB/ZSr+s6PeHF86MRSueiWrYZsBLhfeRrfTLin5zXvpiLO98otC+lizOVvzViT641We13HPPPfkTQdkKw+z8wKx94AMfyF/s9+UvfzkefPDBWg+RBpTtIPMP//APccstt+QvHs1+p1yyZEn+xGRWhM5WtwL+rryRPMpkwVvxtQE0I4W/XtDW1pa3N5o9/bno31qdZ7uzot8Tj8wtvJ+lA6pTSFolK/r9+cninzTtGlTleT23IB6f/WLh/XRWeV5Z0W/W08XPK9M5uDqFv1Wyot+sOcXPbeGAqKqs6DdjXvHz6ir2NRBryIp+01+swryGVXe7t6zo9+jLxc+rNPrZqKasOPbkkuL77N9SncLf385r9qvVyfKV4bOiWrKi3yvLq9Pfsng+qiUr+i1b8WihfSxbUd2vwUbb6rNasq08hw8fnv85K/49//zzsd1228W4ceNi1qzq/V2juWSrELInJLMXlWZfc9dcc03cdtttccghh8QBBxwQDzzwQIwZMyaaXbZiI3vR7de+9rX872Qza9Ys/F1JyaNMFqnu7u78+8MJJ5wQ73rXu6KZ+dpI+dpIyePtNevPHPWuulUBAACgflb8Vdrq2I477hgPP/xw/uc99tgjLr744vjDH/4Q5557bowfP77Ww6NBfeUrX4nzzjsvLrjggtWP7bnnnnH77bfHZz/72aZ6QnJtsidnf/jDH8a+++4bTz75ZDSzZs3C35WUPMpkkb6I6dhjj41LL700rr766mh2vjbKfG2k5LFumvVnjnpnxR8AAMDrzjzzzHj11VfzP2fFvg996EOxzz77xEYbbZQf6A5FyLYby9obvf/9788b5Sdqb7755jj88MPzJ5+ybXmzFbnNqFmz8HclJY8yWazU2dkZn/zkJ+OnP/1pfOELX8hfwNTsfG2s5GsjJY9116w/c9Q7hT8AACDVxGf8Zds+rbLNNtvEY489FgsWLIgNN9wwWlqqu9U4NJOzzjorXnnllXVemXvffffFfvvtF3fddVfDPfkkC6BSRx11VNx0000xYsSI/OeWL33pS+t03ymnnBLvfOc7Cx8fteNrIyWPlfzM0bgU/gAAgEQzn/H3ZkaOHFnrIUDDy7bYeu6559brnvnz58eMGTMa7oknWQCVnlX2m9/8Jv/zwoUL44orrljnez/2sY81VDGDlK+NlDzK/MzRuBT+AACAaPYVf8cff/w6fdyPfvSjwscCzejZZ59dp4/LtuLNtuDNnrCbPHlyHHHEEdFoZAFUorW1NX7961/HwQcfnBc2fvWrXzXVVpa8NV8bKXmU+ZmjcbXWegAAAAC1NnXq1Jg2bVr89a9/zbe7easG1M7ixYvj7/7u7/InnS688MI4++yzo1nJAngzu+66a37+Vv/+/fOixt13313rIdFH+NpIyWPdNevPHA888EB8//vfX+PxmTNnxuWXXx59nRV/AABANPuKv89//vPxk5/8JJ566qn4zGc+E8ccc4wtPqGPeeKJJ+KRRx6JSy+9NE499dRoZrIA3spOO+2Uv5jpoIMOylc17b333rUeEn2Er42UPNZNs/7MccYZZ8Stt94ar7322urHpk+fHgceeGAsXbo0jjzyyBg7dmz0VQp/AABANPsZf9nZHtkvszfeeGO+neekSZPisMMOixNOOCE++MEPRktLS62HCE1vl112yZ98GjVqVDQ7WQBrs+OOO+ZPUPsewRv52kjJ4+01688c119/fRx66KExceLEGD16dP7YAQccEB0dHXHbbbf16aJfxlafAADAm6/4q7TVqba2tjj66KPj9ttvzw+sf/e73x1f+MIXYsstt4wlS5bUenhARNM96bQ2sgDWxvcI3oqvjZQ83l4zZjR8+PB8xV9W7HvppZfyxzo7O+OOO+6IPffcM/o6hT8AAIA3aG1tzVf5lUql6OrqqvVwAAAAqKKhQ4fGLbfckp8DudFGG+Vbwu6+++5RDxT+AACAN93qs9JWr5YvX56f85f9YrftttvmZ1n8y7/8S8yZMyeGDRtW6+HRJKZOnZoXnIG183clJY8yWfBWfG0A62vw4MH51p4vv/xyvu1pvXDGHwAAkOrJlp11+lxKtqXnf/7nf+ZnNRx//PF5AbAZt7QBAACgvin8AQAA0eyFv+9///uxxRZbxPjx4+Ouu+7K25u58cYbqz42AAAAWFcKfwAAQNP79Kc/nZ/pBwAAAPVM4Q8AAEhk5a9KS2AtdXzmCwAAANQ7hT8AACCafatPAAAAaAQKfwAAQKKltLJVei8AAABQG6016hcAAAAAAADoRVb8AQAAKVt9AgAAQF1S+AMAANakgAcAAAB1R+EPAABIOOMPAAAA6pMz/gAAAAAAAKABWPEHAACknPEHAAAAdUnhDwAASNjqEwAAAOqTwl+Bul5+OVpaBhTbxyvDXr++El0vvRRFm3b17VEV/d8VERPiynN+HtE5IxpGPq/j4oeTbmzAeR0bV0/8WWPNa/Xcjol//+JPG2tur8/r54c35rz+6+8bcV7Hxc37NeL3jojLt/hDQ87r0i3uabB5LcovhwxZFDHwlSp1Orj4Lvq35ZetB7RFtBTb36KB3VE3rPgDAACAuqTwBwAAJKz4AwAAgPrUWusBAAAAAAAAAD1nxR8AAJCy1ScAAADUJYU/AAAgpfAHAAAAdUnhDwAASDjjDwAAAOqTM/4AAAAAAACgAVjxBwAApGz1CQAAAHVJ4Q8AAEi0lEp5q/ReAAAAoDYU/gAAgJQVfwAAAFCXnPEHAADUhfPPPz/23nvvGDJkSGywwQZv+jFz5syJww47LP+YjTfeOL72ta9FZ2dn1ccKAAAAtWDFHwAAkGgprWyV3luUjo6OOOqoo2KvvfaKq666ao33d3V15UW/TTfdNO6+++544YUX4tOf/nQMGDAgLrjgguIGBgAAAH2Ewh8AAFAXW31Onjw5v06dOvVN33/bbbfFjBkz4o477ohNNtkkdt555/jWt74VX//61+Occ86JgQMHFjc4GsasWbNi2LBh0exmzpyZXJudPMpkkZJHSh5lskjJo0wWKXmk5FEmizV/T1lXCn8AAECvr/hbtGhR8nhbW1veinTPPffEe97znrzot8ohhxwSn//852P69Omxyy67FNo/9emKK67IW7ZiNHPSSSfVekh9yjHHHFPrIfQp8iiTRUoeKXmUySIljzJZpOSRkkeZLNafwh8AANDrxo4dm7z9zW9+M191V6R58+YlRb/Mqrez98GbOfnkk/OWFatHjBgRV155Zey2227R7LJXVmdPslx77bWxww47RLOTR5ksUvJIyaNMFil5lMkiJY+UPMpkkbr//vvX+UWKCn8AAECvb/U5d+7caG9vX/3wW632O/300+Oiiy5621/4tt9++woHBOtnu+22i1133bXWw+gzsidZ5FEmjzJZpOSRkkeZLFLyKJNFSh4peZTJYqUlS5bEulL4AwAAen2rz6zo97eFv7cyceLEmDBhwlo/Zvz48evU96abbhr33ntv8tj8+fNXvw8AAAAancIfAADQ6yv+1tXo0aPz1hv22muvOP/88+PFF1+MjTfeOH/s9ttvzwuQ73rXu3qlDwAAAOjLFP4AAIC6MGfOnFiwYEF+7er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      "text/plain": [
       "<Figure size 1800x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Visualization for Cliff Walking (SARSA) ---\n",
    "# Create a figure for multiple plots\n",
    "fig_cliff, axs_cliff = plt.subplots(2, 2, figsize=(18, 8)) # Adjusted size slightly\n",
    "\n",
    "# 1. Plot Rewards\n",
    "plot_rewards(cliff_rewards_sarsa, ax=axs_cliff[0, 0])\n",
    "axs_cliff[0, 0].set_title(\"SARSA: Cliff Walking Rewards\")\n",
    "axs_cliff[0, 0].grid(True)\n",
    "\n",
    "# 2. Plot Episode Lengths\n",
    "axs_cliff[0, 1].plot(cliff_lengths_sarsa)\n",
    "axs_cliff[0, 1].set_xlabel('Episode')\n",
    "axs_cliff[0, 1].set_ylabel('Episode Length')\n",
    "axs_cliff[0, 1].set_title('SARSA: Cliff Walking Episode Lengths')\n",
    "axs_cliff[0, 1].grid(True)\n",
    "\n",
    "# Plot Max Q-Values (Heatmap) - Need a function that takes cliff env params\n",
    "def plot_q_values_cliff(q_table, rows, cols, ax):\n",
    "    q_values = np.zeros((rows, cols))\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state = (r, c)\n",
    "            if state in q_table and q_table[state]:\n",
    "                q_values[r, c] = max(q_table[state].values())\n",
    "            else:\n",
    "                q_values[r,c] = -np.inf # Mark unvisited/terminal\n",
    "\n",
    "    # Mask cliff states for better visualization\n",
    "    q_values_masked = np.ma.masked_where(q_values <= -100, q_values) # Hide extreme negatives from cliff\n",
    "\n",
    "    im = ax.imshow(q_values_masked, cmap='viridis')\n",
    "    plt.colorbar(im, ax=ax, label='Max Q-Value')\n",
    "    ax.set_title('SARSA: Max Q-Values (Cliff)')\n",
    "    ax.set_xticks(np.arange(cols))\n",
    "    ax.set_yticks(np.arange(rows))\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ax.grid(which='major', color='w', linestyle='-', linewidth=1)\n",
    "\n",
    "plot_q_values_cliff(cliff_q_table_sarsa, cliff_rows, cliff_cols, ax=axs_cliff[1, 0])\n",
    "\n",
    "\n",
    "# 4. Plot Policy\n",
    "plot_policy_grid(cliff_q_table_sarsa, cliff_rows, cliff_cols, [cliff_terminal_state], ax=axs_cliff[1, 1]) # Pass terminal state\n",
    "axs_cliff[1, 1].set_title(\"SARSA: Learned Policy (Cliff)\")\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Key Observations:**  \n",
    "\n",
    "1. **Top-Left: Rewards Over Episodes**  \n",
    "   - The total reward **improves significantly** over time, indicating successful learning.  \n",
    "   - Early episodes show **high volatility**, with large negative rewards, likely due to frequent falls into the cliff.  \n",
    "   - As training progresses, the rewards stabilize closer to **zero**, suggesting the agent has learned a near-optimal policy.  \n",
    "\n",
    "2. **Top-Right: Episode Length Over Time**  \n",
    "   - Initially, episode lengths are **very high** (over 200 steps), meaning the agent is struggling to reach the goal efficiently.  \n",
    "   - As learning progresses, episode lengths **decrease rapidly**, stabilizing around 20–30 steps after about 200 episodes.  \n",
    "   - Occasional spikes indicate **exploration-based deviations**, but overall, the agent converges toward an optimal route.  \n",
    "\n",
    "3. **Bottom-Left: Max Q-Values Per State (Heatmap)**  \n",
    "   - The **Q-values are lowest in the cliff region**, confirming that the agent recognizes the risk.  \n",
    "   - The **upper areas of the grid have higher Q-values**, indicating safer and more rewarding paths.  \n",
    "   - The **goal state has the highest Q-values**, reinforcing its desirability.  \n",
    "\n",
    "4. **Bottom-Right: Learned Policy (Arrow Grid)**  \n",
    "   - The **arrows indicate the agent’s optimal action at each state**.  \n",
    "   - The agent follows a predominantly **rightward (→) and downward (↓) strategy**, prioritizing an efficient route to the goal.  \n",
    "   - In the **cliff-adjacent row**, the agent takes a more cautious approach, often moving **up (↑) or left (←)** to avoid falling off.  \n",
    "   - The terminal state (\"T\") at the bottom-right marks the goal.  \n",
    "\n",
    "**Analysis:**  \n",
    "- The **agent successfully learns an optimal policy**, balancing exploration and exploitation.  \n",
    "- Early exploration leads to **high failure rates**, but SARSA enables the agent to refine its policy toward safer decisions.  \n",
    "- **Q-value distribution confirms risk-awareness**, avoiding the cliff while optimizing rewards.  \n",
    "- Compared to **Q-learning, SARSA typically favors safer paths**, which aligns with the observed policy adjustments near dangerous regions.  \n",
    "\n",
    "Overall, the SARSA agent learns to **navigate the cliff environment efficiently**, minimizing penalties while improving its success rate."
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    "## Common Challenges and Solutions\n",
    "\n",
    "**Challenge: Slow learning or getting stuck**\n",
    "*   **Solution**: Tune hyperparameters (α, γ, ε decay). Increase exploration initially or use more sophisticated exploration (e.g., Boltzmann). Function approximation might be needed for very large state spaces (though not used here).\n",
    "\n",
    "**Challenge: Balancing exploration and exploitation**\n",
    "*   **Solution**: Use a well-tuned epsilon decay schedule. Start high (near 1.0) and decay to a small value (e.g., 0.01 or 0.1). The decay rate determines how quickly the agent shifts from exploration to exploitation.\n",
    "\n",
    "**Challenge: Choosing appropriate hyperparameters**\n",
    "*   **Solution**: Experimentation is key. Common starting points:\n",
    "    *   α (Learning Rate): 0.1 is often a good start. Higher values learn faster but can be unstable.\n",
    "    *   γ (Discount Factor): 0.9 to 0.99. Higher values emphasize future rewards more.\n",
    "    *   ε (Epsilon): Start high (1.0), decay to low (0.1 or 0.01). Adjust decay rate.\n",
    "\n",
    "**Challenge: Sensitivity to initial Q-values**\n",
    "*   **Solution**: Initialize Q-values optimistically (to encourage exploration) or to zero. Consistency is important.\n",
    "\n",
    "## SARSA vs. Other Reinforcement Learning Algorithms\n",
    "\n",
    "### Advantages of SARSA\n",
    "-   **On-Policy:** Learns the value of the policy being executed, which can be useful for policy evaluation or in situations where understanding the current behavior is important.\n",
    "-   **Often More Stable/Conservative:** Because it accounts for exploration steps in its updates, it can be less prone to learning overly optimistic/risky policies compared to Q-Learning, especially near hazards (like the cliff).\n",
    "-   **Guaranteed Convergence:** Converges under similar conditions as Q-learning (if all state-action pairs are visited infinitely often and learning rate decays appropriately).\n",
    "-   Relatively simple to understand and implement.\n",
    "\n",
    "### Limitations of SARSA\n",
    "-   **Can be Slower:** Learning the optimal policy might take longer than Q-learning because it learns based on the potentially suboptimal exploratory actions taken.\n",
    "-   **Suboptimal Policy during Learning:** Since it learns based on the current (possibly exploratory) policy, the learned policy during the intermediate stages might be less optimal than what Q-learning might estimate.\n",
    "-   **Same State Space Limitations:** Like Q-Learning, tabular SARSA struggles with very large or continuous state/action spaces.\n",
    "\n",
    "### Related Algorithms\n",
    "-   **Q-Learning**: The classic off-policy TD control algorithm. Learns the optimal action-value function directly.\n",
    "-   **Expected SARSA**: A variation that uses the *expected* Q-value of the next state (averaging over actions according to the policy) instead of the single sampled next action's Q-value. Often performs better than SARSA.\n",
    "-   **SARSA(λ) / True Online SARSA(λ)**: Incorporates eligibility traces to speed up learning by propagating updates back through sequences of states and actions.\n",
    "-   **Actor-Critic Methods**: Combine value-based approaches (like SARSA/Q-Learning) with policy-based approaches, learning both a value function and a policy explicitly.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "SARSA is a fundamental on-policy temporal difference (TD) control algorithm in reinforcement learning. Its key characteristic is updating Q-values based on the action actually taken in the next state according to the current policy (State-Action-Reward-State-Action quintuple).\n",
    "\n",
    "This on-policy nature often leads to more conservative but potentially more stable learning compared to its off-policy counterpart, Q-learning, especially in environments with significant risks. As demonstrated in the Cliff Walking example, SARSA tends to learn safer paths. While tabular SARSA shares limitations with Q-learning regarding large state spaces, its principles form the basis for more advanced on-policy algorithms used in complex domains. Understanding SARSA provides valuable insight into the core concepts of on-policy learning and TD control."
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