🎯 NeatRL

A clean, modern Python library for reinforcement learning algorithms

NeatRL provides high-quality implementations of popular RL algorithms with a focus on simplicity, performance, and ease of use. Built with PyTorch and designed for both research and production use.

✨ Features

• 📊 Experiment Tracking: Built-in support for Weights & Biases logging
• 🎮 Gymnasium Compatible: Works with Gymnasium environments and adding many more!
• 🎯 Atari Support: Full support for Atari games with automatic CNN architectures
• ⚡ Parallel Training: Vectorized environments for faster data collection
• 🔧 Easy to Extend: Modular design for adding new algorithms
• 📈 State-of-the-Art: Implements modern RL techniques and best practices
• 🎥 Video Recording: Automatic video capture and WandB integration
• 📉 Advanced Logging: Per-layer gradient monitoring and comprehensive metrics

🏗️ Supported Algorithms

Current Implementations

• DQN (Deep Q-Network) - Classic value-based RL algorithm

  • Support for discrete action spaces
  • Experience replay and target networks
  • Atari preprocessing and frame stacking

• Dueling DQN - Enhanced DQN with separate value and advantage streams

  • Improved learning stability
  • Better performance on complex environments

• REINFORCE - Policy gradient method for discrete and continuous action spaces

  • Atari game support with automatic CNN architecture
  • Parallel environment training (n_envs support)
  • Continuous action space support
  • Episode-based Monte Carlo returns
  • Variance reduction through baseline subtraction

• DDPG (Deep Deterministic Policy Gradient) - Actor-critic method for continuous action spaces

  • Deterministic policy gradient for continuous control
  • Experience replay and target networks
  • Ornstein-Uhlenbeck noise for exploration
  • Support for exact continuous action spaces

• A2C (Advantage Actor-Critic) - Synchronous actor-critic algorithm

  • Synchronous version of A3C for stable training
  • Advantage function for reduced variance
  • Support for both discrete and continuous action spaces
  • Parallel environment training with vectorized environments
  • Monte Carlo returns for value estimation

• TD3 (Twin Delayed DDPG) - Advanced actor-critic method for continuous control

  • Twin Q-networks to reduce overestimation bias
  • Delayed policy updates for improved stability
  • Target policy smoothing with noise
  • Experience replay and target networks
  • CNN support for image-based environments

• SAC (Soft Actor-Critic) - Maximum entropy reinforcement learning

  • Stochastic Gaussian policies with entropy regularization
  • Twin Q-networks for stable learning
  • Automatic entropy tuning (alpha parameter)
  • Balances exploration and exploitation
  • CNN support for complex environments

• PPO (Proximal Policy Optimization) - State-of-the-art policy gradient method with GAE

  • Full PPO implementation with Generalized Advantage Estimation (GAE)
  • Support for both discrete and continuous action spaces
  • Atari game support with automatic CNN architecture
  • Clipped surrogate objective for stable policy updates
  • Value function clipping and entropy regularization
  • Vectorized environments for parallel training

• PPO-RND (Proximal Policy Optimization with Random Network Distillation) - State-of-the-art exploration method

  • Intrinsic motivation through novelty detection
  • Combined extrinsic and intrinsic rewards for better exploration
  • Support for both discrete and continuous action spaces
  • PPO with clipped surrogate objective
  • Vectorized environments for parallel training
  • Intrinsic reward normalization and advantage calculation

• More algorithms coming soon...

📦 Installation

python -m venv neatrl-env
source neatrl-env/bin/activate 

pip install neatrl

# Install extras based on environments you want to use
pip install neatrl[atari]      # For CarRacing
pip install neatrl[box2d]      # For BipedalWalker
pip install neatrl[classic]    # For Pendulum
pip install neatrl[mujoco]     # For HalfCheetah

🚀 Quick Start

Train DQN on CartPole

from neatrl import train_dqn

model = train_dqn(
    env_id="CartPole-v1",
    total_timesteps=10000,
    seed=42
)

Train PPO on Classic Control

from neatrl import train_ppo

model = train_ppo(
    env_id="CartPole-v1",
    total_timesteps=50000,
    n_envs=4,           # Parallel environments
    GAE=0.95,           # Generalized Advantage Estimation lambda
    clip_value=0.2,     # PPO clipping parameter
    use_wandb=True,     # Track with WandB
    seed=42
)

Train SAC on Continuous Control

from neatrl import train_sac

model = train_sac(
    env_id="Pendulum-v1",
    total_timesteps=50000,
    alpha=0.2,          # Entropy regularization coefficient
    autotune_alpha=True, # Automatically tune alpha
    use_wandb=True,     # Track with WandB
    seed=42
)

Train SAC on Atari

from neatrl import train_sac_cnn

model = train_sac_cnn(
    env_id="BreakoutNoFrameskip-v4",
    total_timesteps=100000,
    alpha=0.2,
    autotune_alpha=True,
    atari_wrapper=True, # Automatic Atari preprocessing
    use_wandb=True,     # Track with WandB
    seed=42
)

📚 Documentation

📖 Complete Documentation

The docs include:

• Detailed usage examples
• Hyperparameter tuning guides
• Environment compatibility
• Experiment tracking setup
• Troubleshooting tips

🤝 Contributing

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

Development Setup

git clone https://github.com/YuvrajSingh-mist/NeatRL.git
cd NeatRL
pip install -e .[dev]

For the complete changelog, see CHANGELOG.md.

📄 License

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

Made with ❤️ for the RL community