19th September 2023

nanoPPO 0.13 Release the Proximal Policy Optimization (PPO) algorithm for reinforcement learning is now available. Initially supporting discrete action spaces in v0.1, the latest v0.13 has expanded its support to continuous action spaces, catering to a broader spectrum of applications. To aid users in comprehending the training process, the release is equipped with examples that demonstrate how agents can be trained across different environments. Besides MountainCarContinuous, two unique customized environments, namely PointMass1D and PointMass2D, have been introduced. These are specifically designed to facilitate the convenient testing of PPO agent training. An initial test suite is incorporated to maintain high standards of code quality and ensure consistent functionality. For a comprehensive overview, please refer to the Github readme and the Github release notes.