py-rl-toolkit 0.1.0

A comprehensive, easy-to-use Python toolkit for reinforcement learning research and education

PyPiRL - Python Reinforcement Learning Toolkit

A comprehensive, easy-to-use Python toolkit for reinforcement learning research and education. Built with clean APIs, extensive documentation, and thorough testing.

🚀 Features

Core Algorithms

• Q-Learning: Tabular off-policy value-based algorithm
• SARSA: Tabular on-policy value-based algorithm
• DQN: Deep Q-Network with PyTorch neural networks

Environments

• SimpleGridWorld: Configurable grid environment with obstacles and goals
• SimpleMaze: Customizable maze environment with walls

Policies

• RandomPolicy: Uniform random action selection
• GreedyPolicy: Always selects best action
• EpsilonGreedyPolicy: Balances exploration/exploitation with decay
• SoftmaxPolicy: Boltzmann exploration with temperature

Utility Functions

• Training: train_agent() with progress tracking and early stopping
• Evaluation: evaluate_agent() with performance metrics
• Visualization: plot_learning_curve() and plot_comparison()
• Persistence: save_agent() and load_agent() for model saving
• Episode Running: run_episode() for single episode execution
• Algorithm Comparison: compare_algorithms() for benchmarking

📦 Installation

From PyPI (when published)

pip install py-rl-toolkit

From Source

git clone https://github.com/Nits1627/PyPiRL.git
cd PyPiRL
pip install -e .

From Wheel

pip install dist/rltoolkit-0.1.0-py3-none-any.whl

🎯 Quick Start

from rltoolkit import QLearning, SimpleGridWorld, EpsilonGreedyPolicy

# Create environment and agent
env = SimpleGridWorld(size=5)
agent = QLearning(env.state_space_size, env.action_space_size)
policy = EpsilonGreedyPolicy(agent, epsilon=0.1)

# Train the agent
from rltoolkit import train_agent
rewards = train_agent(env, agent, policy, episodes=100)

# Evaluate performance
from rltoolkit import evaluate_agent
avg_reward = evaluate_agent(env, agent, policy, episodes=10)
print(f"Average reward: {avg_reward}")

📚 Examples

See examples.py for comprehensive usage examples including:

• Training different algorithms
• Comparing algorithm performance
• Visualizing learning curves
• Custom environment creation

🧪 Testing

Run the comprehensive test suite:

python -m pytest tests/ -v

📖 Documentation

Detailed documentation is available in docs.md including:

• API reference for all classes and functions
• Algorithm explanations
• Environment specifications
• Policy implementations

🔧 Requirements

• Python ≥ 3.7
• NumPy
• Matplotlib
• PyTorch (for DQN)
• TQDM (for progress bars)

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📊 Package Status

• ✅ All 58 tests passing
• ✅ Package successfully built
• ✅ Ready for PyPI publication
• ✅ GitHub Actions workflow configured for automated publishing