Algorithms for Policy Evaluation, Estimation of Action Values, Policy Improvement, Policy Iteration, Truncated Policy Evaluation, Truncated Policy Iteration, Value Iteration . From Udacity's Deep Reinforcement Learning Nanodegree program.

Implementation of RL Algorithms in Openai Gym Frozen-Lake Environment

Implementation of several algorithms in RL based on Prof. sutton's book

Jupyter notebooks implementing Reinforcement Learning algorithms in Numpy and Tensorflow

Fourth assignment for Machine Learning course @USI19/20.

A web-based interactive Grid World environment for learning and visualizing reinforcement learning algorithms including policy evaluation, policy improvement, and value iteration. Built with Flask backend implementing RL algorithms and JavaScript frontend for grid visualization.

Various reinforcement learning algorithms implemented on the frozen lake grid world.

Based on the book --- Reinforcement Learning: An Introduction (2nd ed, 2018) by Sutton and Barto. For the Reinforcement Learning course Assignment 2 (see Gridworld Problem 1.pdf) at Memorial University of Newfoundland, Jul. 18, 2024

This repository contains two reinforcement learning projects: "Treasure Hunt in the Frozen Lake," which navigates a modified FrozenLake using dynamic programming, and "Optimizing Movie Recommendations," which employs Multi-Armed Bandits to enhance user satisfaction.

A reinforcement learning agent trained using Q-Learning to solve OpenAI Gym’s FrozenLake environment. The project demonstrates value-based learning, policy improvement, and exploration strategies in a slippery gridworld setting.