Unified Algorithm Interface
All reinforcement learning algorithms share a consistent interface, simplifying model initialization, training, saving, and loading.
High Code Quality and Testing
The codebase follows PEP8 style guidelines, includes type hints, and has automated unit tests covering 95% of the code to ensure reliability.
Tensorboard Support
Integrated Tensorboard support allows users to monitor training metrics and visualize performance during model training.
Support for Multiple Observation Spaces
Supports Box, Discrete, MultiDiscrete, MultiBinary, and Dict observation spaces, enabling flexibility in environment design.
Compatibility with Gymnasium
Uses Gymnasium as the primary backend with compatibility for Gym environments via shimmy, facilitating migration and environment support.