Python offers several libraries for implementing Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) models. Here are some of the most popular and widely used libraries:
1. KerasRL:
- Description: KerasRL is a deep reinforcement learning library built on top of the Keras deep learning library. It provides a set of high-level APIs for building and training reinforcement learning models. KerasRL includes implementations of popular algorithms like Deep Q-Networks (DQN) and Actor-Critic methods[1][3].
- Criteria:
- State of the art RL methods: Implemented algorithms include DQN, DDPG, CDQN, CEM, and Deep SARSA.
- Easy to start: The code is well-commented and easy to read, making it simple to understand and modify.
- Easy to plug your own environment: Not agnostic to OpenAI Gym, so modifications are required for other environments.
- Easy to modify the agents: Very easy to add new agents.
- Community and updates: Not actively maintained.
- Tensorboard support: Not implemented.
- Other features: Includes a vectorized environment system[4].
2. RL_Coach:
- Description: RL_Coach is a Python RL framework developed by Intel AI Lab. It contains many state-of-the-art algorithms and exposes easy-to-use APIs for experimenting with new RL algorithms. The components are modular, making it easy to extend and reuse existing components[1].
- Criteria:
- State of the art RL algorithms implemented: Includes a wide range of algorithms such as Actor-Critic, ACER, Behavioral Cloning, and more.
- Official documentation and tutorials: Complete documentation and valuable tutorials are available.
- Readable code: The code is open-source but lacks comments, making customization challenging.
- Number of supported environments: Not specified.
- Logging and tracking tools: Not specified.
- Vectorized environment: Not specified.
- Regular updates: Not specified[1].
3. Tensorforce:
- Description: Tensorforce is a deep reinforcement learning framework based on TensorFlow. It includes one of the best implementations of RL algorithms[4].
- Criteria:
- State of the art RL methods: Not specified.
- Good documentation and tutorials: Not specified.
- Works in your own environment: Not specified.
- Legible code: Not specified.
- Regular updates and active community: Not specified.
- Tensorboard support: Not specified.
- Other features: Not specified[4].
4. Pyqlearning:
- Description: Pyqlearning is a Python library for implementing Reinforcement Learning and Deep Reinforcement Learning, especially for Q-Learning, Deep Q-Network, and Multi-agent Deep Q-Network. It can be optimized by Annealing models such as Simulated Annealing, Adaptive Simulated Annealing, and Quantum Monte Carlo Method[2].
- Criteria:
- State of the art RL methods: Not specified.
- Official documentation and tutorials: Not specified.
- Readable code: Not specified.
- Number of supported environments: Not specified.
- Logging and tracking tools: Not specified.
- Vectorized environment: Not specified.
- Regular updates: Not specified[2].
5. Stable Baselines:
- Description: Stable Baselines is a powerful library that offers a collection of state-of-the-art reinforcement learning algorithms in Python. It is built on top of the OpenAI Gym library and provides a simple and easy-to-use API for training and evaluating reinforcement learning agents[3].
- Criteria:
- State of the art RL methods: Includes implementations of popular algorithms like A2C, TRPO, and PPO.
- Good documentation and tutorials: Not specified.
- Works in your own environment: Not specified.
- Legible code: Not specified.
- Regular updates and active community: Not specified.
- Tensorboard support: Not specified.
- Other features: Not specified[3].
6. Ray RLlib:
- Description: Ray RLlib is a reinforcement learning library built on top of the Ray distributed computing framework. It provides a comprehensive set of APIs for building and training reinforcement learning models, including support for distributed training and hyperparameter tuning[3].
- Criteria:
- State of the art RL methods: Includes implementations of popular algorithms like DQN, A3C, and IMPALA.
- Good documentation and tutorials: Not specified.
- Works in your own environment: Not specified.
- Legible code: Not specified.
- Regular updates and active community: Not specified.
- Tensorboard support: Not specified.
- Other features: Not specified[3].
7. Dopamine:
- Description: Dopamine is a reinforcement learning library developed by Google. It is designed to provide a flexible and easy-to-use framework for building and training reinforcement learning agents. Dopamine includes implementations of popular algorithms like DQN and C51, as well as tools for visualizing and analyzing your reinforcement learning experiments[3].
- Criteria:
- State of the art RL methods: Includes implementations of popular algorithms.
- Good documentation and tutorials: Not specified.
- Works in your own environment: Not specified.
- Legible code: Not specified.
- Regular updates and active community: Not specified.
- Tensorboard support: Not specified.
- Other features: Not specified[3].
8. RLkit:
- Description: RLkit is a reinforcement learning library developed by the Berkeley Artificial Intelligence Research (BAIR) Lab. It is built on top of PyTorch and provides a set of high-level APIs for building and training reinforcement learning models. RLkit includes implementations of popular algorithms like DDPG and SAC, as well as tools for visualizing and analyzing your results[3].
- Criteria:
- State of the art RL methods: Includes implementations of popular algorithms.
- Good documentation and tutorials: Not specified.
- Works in your own environment: Not specified.
- Legible code: Not specified.
- Regular updates and active community: Not specified.
- Tensorboard support: Not specified.
- Other features: Not specified[3].
Each library has its strengths and weaknesses, and the choice of which one to use depends on the specific requirements of your project. For example, if you need a library with a wide range of implemented algorithms, RL_Coach might be a good choice. If you prefer a library built on top of TensorFlow, Tensorforce could be suitable. For a library with a simple and easy-to-use API, Stable Baselines might be the best option[1][3][4].
Citations:[1] https://neptune.ai/blog/the-best-tools-for-reinforcement-learning-in-python
[2] https://pypi.org/project/pyqlearning/
[3] https://www.devopsschool.com/blog/list-of-reinforcement-learning-libraries/
[4] https://blog.dataiku.com/on-choosing-a-deep-reinforcement-learning-library
[5] https://pub.towardsai.net/top-python-packages-for-studying-reinforcement-learning-e332e1c6e16a?gi=9d11806ea7b2