Huskarl is a modular deep reinforcement learning framework designed for rapid prototyping and efficient parallelization of environment interactions. It targets researchers and practitioners working with computationally intensive environments, offering a streamlined way to implement and test various RL algorithms.

How It Works

Built on TensorFlow 2.0 and tf.keras, Huskarl emphasizes modularity for easy algorithm and agent customization. Its core advantage lies in its ability to parallelize environment dynamics computation across multiple CPU cores, significantly accelerating on-policy learning algorithms like A2C and PPO, especially in demanding simulations.

Quick Start & Requirements

• Install from source: git clone https://github.com/danaugrs/huskarl.git && cd huskarl && pip install -e .
• PyPI install: pip install huskarl
• Prerequisites: matplotlib, gym.
• Examples require matplotlib and gym.

Highlighted Details

• Implemented algorithms: DQN, Multi-step DQN, Double DQN, Dueling DQN, A2C, DDPG, Prioritized Experience Replay.
• Planned algorithms: PPO, Curiosity-Driven Exploration.
• Seamless integration with OpenAI Gym environments.
• Plans to support multi-agent and Unity3D environments.

Maintenance & Community

• Project initiated in 2019 by Daniel Salvadori.
• No explicit community channels (Discord/Slack) or roadmap links provided in the README.

Licensing & Compatibility

• The README does not explicitly state a license. The repository's default license is likely MIT unless specified otherwise in a LICENSE file.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The framework is still under active development, with several key algorithms like PPO not yet implemented. The README does not specify a license, which could impact commercial adoption. Community support channels and detailed documentation beyond the README are not readily apparent.