TF2RL is a comprehensive deep reinforcement learning library built with TensorFlow 2.x, offering implementations of various model-free and model-based RL algorithms, as well as imitation learning techniques. It targets researchers and practitioners needing a flexible framework for experimenting with and deploying RL agents across discrete and continuous action spaces.

How It Works

TF2RL provides a modular structure, allowing users to easily integrate and experiment with different algorithms, such as PPO, DQN variants, DDPG variants, SAC, CURL, MPC, ME-TRPO, GAIL, and VAIL. The library supports advanced techniques like Generalized Advantage Estimation (GAE), ApeX for distributed training, and spectral normalization for GANs, facilitating efficient and stable learning.

Quick Start & Requirements

• Install: pip install tf2rl
• Dependencies: TensorFlow 2.x. GPU support requires compatible NVIDIA drivers and CUDA.
• Docker: Pre-built CPU and GPU (Linux-only, experimental) containers are available.
• Examples: Detailed examples for training agents are provided in the examples/ directory.
• Logging: Training progress can be monitored via TensorBoard (tensorboard --logdir results).

Highlighted Details

• Implements a wide range of state-of-the-art RL algorithms, including PPO, DQN, DDPG, SAC, CURL, MPC, ME-TRPO, GAIL, and VAIL.
• Supports both discrete and continuous action spaces, with specific implementations for various algorithm variants.
• Includes advanced techniques like GAE, ApeX, and spectral normalization.
• Offers pre-built Docker containers for easier setup and execution.

Maintenance & Community

The project is maintained by Kei Ota. Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should verify licensing for commercial or closed-source use.

Limitations & Caveats

GPU support via Docker is experimental and has known issues with ApeX multiprocess learning. The README does not specify compatibility with specific TensorFlow versions beyond 2.x or other major deep learning frameworks.