Overview
Gymnasium provides an API standard for single-agent reinforcement learning environments, offering popular reference environments and related utilities as a modern successor to OpenAI Gym. The project serves as a stable baseline for training, evaluating, and researching RL algorithms.
Key Features
- Standardized API: Unified environment interface for reproducible experiments and algorithm comparisons.
- Reference environments: Includes many common RL environments and supporting utilities.
- Community & ecosystem: Maintained by the Farama Foundation with active contributor engagement.
Use Cases
- RL research: Quickly set up training and evaluation baselines for experiments.
- Teaching & demos: Reproducible environments for classroom examples and algorithm instruction.
- Simulation & benchmarking: Standardized platform for comparing training strategies and algorithm performance.
Technical Details
- Stack: Python-first ecosystem compatible with mainstream RL toolchains and dependencies.
- Extensibility: Environments and tools are easy to extend and adapt to new scenarios.
- License: MIT license suitable for research and commercial use.