OpenAI Baselines provides high-quality implementations of reinforcement learning algorithms, targeting researchers and practitioners. It aims to facilitate replication, refinement, and the establishment of strong baselines for new RL research, offering implementations on par with published results for algorithms like DQN.

How It Works

The library offers a suite of RL algorithms including A2C, ACER, ACKTR, DDPG, DQN, GAIL, HER, PPO1, PPO2, and TRPO. It's built with a focus on reproducibility and ease of use, allowing users to train models using a unified command-line interface that specifies the algorithm, environment, and various hyperparameters. The implementations are designed to be modular, enabling researchers to easily integrate and test new ideas.

Quick Start & Requirements

• Install: pip install -e . after cloning the repository.
• Prerequisites: Python >= 3.5, CMake, OpenMPI, zlib. TensorFlow 1.4-1.14 (master branch) or TF 2.0 (tf2 branch). MuJoCo is required for specific environments.
• Setup: Requires system package installation and potentially virtual environment setup.
• Docs: https://github.com/openai/baselines

Highlighted Details

• High-quality implementations of popular RL algorithms.
• DQN and variants achieve scores comparable to published papers.
• Supports training on MuJoCo and Atari environments.
• Includes utilities for saving, loading, and visualizing model performance.

Maintenance & Community

The project is in maintenance mode, expecting bug fixes and minor updates.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README.

Limitations & Caveats

The serialization API for saving/loading models is not fully unified. MuJoCo environments require a proprietary license and specific setup. The master branch has limited TensorFlow version support.