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Implementation of DDPG (Deep Deterministic Policy Gradient) to solve the MountainCarContinuous-v0 environment from OpenAI Gym.

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DDPG-MountainCarContinuous

This repository contains an implementation of the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the MountainCarContinuous-v0 environment from OpenAI Gym.

Overview

The aim of this project is to demonstrate how DDPG, an actor-critic, model-free algorithm based on deep reinforcement learning, can be applied to classic continuous control tasks like MountainCarContinuous-v0.

MountainCarContinuous-v0

Features

  • DDPG implementation using PyTorch (or TensorFlow if you used it).
  • Continuous action space policy for effective control of the car.
  • Script for training and evaluation.
  • Configurable hyperparameters.

Requirements

  • Python 3.6+
  • numpy
  • gym (or gymnasium)
  • torch (if using PyTorch)
  • matplotlib (for plotting, optional)

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Implementation of DDPG (Deep Deterministic Policy Gradient) to solve the MountainCarContinuous-v0 environment from OpenAI Gym.

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