Gymnasium is a suite of standardized APIs and simulation toolkits used to evaluate agent behavior and benchmark reinforcement learning algorithms. It provides a standardized interface for creating and interacting with simulated environments, enabling the training of reinforcement learning agents through a consistent set of interaction protocols.
The project emphasizes experimental reproducibility through a versioned API and a system for tracking changes to environment logic using version suffixes. This ensures that learning results remain consistent and can be replicated across different software releases.
The toolkit includes a collection of reference simulation tasks across physics, text, and game-based scenarios. It supports vectorized environment execution to collect experience in parallel, as well as wrapper-based logic to modify observations or rewards without altering the primary simulation engine.
