NimRL provides the environment for playing the game of Nim and a collection of reinforcement learning algorithms. Currently, NimRL supports most of the algorithms in Chapter 4~7 of Reinforcement Learning: An Introduction (2nd Edition). Besides, Deep Q-Network is implemented based on Keras. Furthermore, any other 2-player games (e.g., board games like Go, Chess, and Tic-Tac-Toe) can be easily extended on NimRL. The games and the Tabular-based Reinfrocement Learning methods are written in C++, while some Deep Reinfrocement Learning methods are written in Python. All of the core APIs are exposed to Python using pybind11.
Install NimRL and start your journal:)