In recent years, the emergence of deep reinforcement learning (RL) has resulted in the growing demand for their evaluation. To implement and test RL models quickly and reliably, several RL libraries have been developed. Pyqlearning is a Python library to implement RL, especially for Q-Learning and multi-agent Deep Q-Network. This library makes it possible to design the information search algorithm such as the Game AI, web crawlers, or robotics. Keras-RL seamlessly implements state-of-the-art deep reinforcement learning algorithms with the deep learning library Keras.

MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible framework to minimize the effort in implementing and testing novel RL methodologies. Indeed, the architecture of MushroomRL is built in such a way that every component of an RL problem is already provided, and most of the time users can only focus on the implementation of their own algorithms and experiments. The result is a library from which RL researchers can significantly benefit in the critical phase of the empirical analysis of their works. MushroomRL stable code, tutorials and documentation can be found at https://github.com/MushroomRL/mushroom-rl.