Top 10 Reinforcement Learning Libraries

Reinforcement Learning is the third paradigm of Machine Learning which is conceptually quite different from the other supervised and unsupervised learning. Although we had a good number of libraries for supervised and unsupervised learning for a long time, it was not the case with reinforcement learning a few years back. Its algorithms had to be coded from scratch, but with its growing popularity many reinforcement learning libraries have come up that can make life easier for RL developers.

Here are ten popular RL libraries (in no particular order) as of my knowledge cutoff in September 2021:

1. TensorFlow:

TensorFlow is a popular open-source library developed by Google. It provides a comprehensive framework for building and training machine learning models, including reinforcement learning algorithms. TensorFlow has a dedicated module called tf-agents that offers RL-specific functionalities.

2. PyTorch:

PyTorch is an open-source deep learning library widely used for various machine learning tasks, including reinforcement learning. It offers a flexible and intuitive interface and supports dynamic computation graphs, making it popular among researchers. Several RL-specific libraries, such as Stable Baselines3 and RLlib, are built on top of PyTorch.

3. OpenAI Gym:

OpenAI Gym is a widely used RL library that provides a collection of standardized environments for benchmarking RL algorithms. It offers a simple and unified interface for interacting with different environments and supports a range of classic control tasks, Atari games, robotics simulations, and more.

4. Stable Baselines3:

Stable Baselines3 is a high-level RL library built on top of PyTorch. It provides a set of stable and well-tested baseline algorithms, such as DQN, PPO, A2C, and SAC, along with tools for training and evaluating RL agents. Stable Baselines3 offers an easy-to-use API and supports parallel training.

5. RLlib:

RLlib is an open-source RL library developed by Ray, an emerging framework for distributed computing. RLlib offers a scalable and efficient infrastructure for RL training and evaluation. It provides a wide range of state-of-the-art algorithms, including DQN, PPO, and IMPALA, and supports distributed training across multiple machines.

6. Dopamine:

Dopamine is an open-source RL framework developed by Google. It focuses on providing a research platform for reliable and reproducible RL experiments. Dopamine includes a set of state-of-the-art baselines, such as DQN and C51, along with easy-to-use interfaces and utilities for building new agents.

7. Keras-RL:

Keras-RL is a high-level RL library built on top of Keras, a popular deep learning library. It offers a simple and modular API for implementing RL algorithms. Keras-RL includes various RL techniques, such as DQN, DDPG, and A3C, and supports customization and experimentation.

8. Garage:

Garage is a toolkit for RL research developed by the Berkeley Artificial Intelligence Research (BAIR) Lab. It provides a wide range of algorithms, interfaces, and utilities to facilitate RL research. Garage aims to support efficient experimentation and reproducibility in RL.

9. Coach:

Coach is an RL library developed by Intel AI Lab. It provides a comprehensive set of building blocks and algorithms for RL. Coach focuses on modularity, allowing users to easily customize and extend the library for specific research or application needs.

10. Unity ML-Agents:

Unity ML-Agents is an open-source toolkit developed by Unity Technologies for training RL agents in Unity environments. It allows researchers and developers to integrate RL into Unity’s 3D simulation environments, enabling the training of agents for tasks like game playing and robotics.

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