CartPole Reinforcement Learning Practice System

A comprehensive implementation of multiple RL algorithms for solving the CartPole-v1 environment.

Setup

1. Create virtual environment:

python -m venv venv
venv\Scripts\activate  # Windows

2. Install dependencies:

pip install -r requirements.txt

3. Verify setup:

python verify_setup.py

Project Structure

cartpole-rl/
├── agents/           # RL algorithm implementations
├── utils/            # Helper functions
├── models/           # Saved model weights
├── logs/             # Training logs and metrics
├── results/          # Plots and visualizations
├── configs/          # Configuration files
├── GUIDE.md          # Step-by-step implementation guide
└── README.md         # This file

Quick Start

Run the random agent baseline:

python agents/random_agent.py

Progress Checklist

• Create virtual environment
• Install dependencies
• Verify Gymnasium installation
• Create project structure
• Implement random baseline
• Implement environment wrapper
• Create training loop framework
• Implement Q-Learning
• Implement DQN
• Implement Policy Gradient
• Implement Actor-Critic
• Implement PPO

Current Status

Phase: 1 - Project Setup ✓
Next: Implement Random Agent baseline

See GUIDE.md for detailed implementation steps.