There are several Python libraries that are useful for RL, including: 1. TensorFlow: TensorFlow is a popular deep learning library that can also be used for RL. It offers a range of tools and resources for RL researchers and practitioners. 2. pyTorch: PyTorch is another popular deep learning library that can also be used for RL. It is similar to TensorFlow, but has a different syntax. 3. OpenAI Gym: OpenAI Gym is a collection of environments that can be used to test and evaluate RL algorithms. It offers a variety of environments including games, simulated robots and financial markets. 4. RLlib: RLlib is a library that provides a high-level interface for building and training RL agents. It is easy to use and scalable. 5. stable baselines: Stable Baselines is a library that provides implementations of a number of RL algorithms, including DQN, PPO and TRPO. It is easy to use and efficient. 6. keras-RL: Keras-RL is a library that provides a way to create and train RL agents with Keras. It is a good choice for researchers who are familiar with Keras. 7. muJoCo: MuJoCo is a physics engine that can be used to create realistic RL environments. It is a good choice for researchers who need to create realistic environments for their experiments. 8 Ray RLlib: Ray RLlib is a distributed RL library built on top of RLlib. It is designed to be scalable and efficient for running RL agents on large datasets.