Gym provides a standard API for developing and comparing reinforcement learning algorithms, serving as a foundational toolkit for researchers and practitioners in the RL field. Its core contribution is a consistent interface for interacting with diverse environments, simplifying algorithm development and benchmarking.

How It Works

Gym models environments as Python classes, offering a straightforward API for interaction. Algorithms send actions to the environment and receive observations, rewards, and termination signals. This abstraction decouples algorithms from environment specifics, promoting code reusability and enabling standardized comparisons across different RL algorithms and environments.

Quick Start & Requirements

• Install base library: pip install gym
• Install environment dependencies: pip install gym[atari] or pip install gym[all]
• Supported Python versions: 3.7-3.10 on Linux and macOS.
• Official documentation: https://www.gymlibrary.dev/
• Discord server: https://discord.gg/nHg2JRN489

Highlighted Details

• Standardized API for RL environments, widely adopted in the research community.
• Strict environment versioning (_v0, _v1, etc.) ensures reproducibility.
• Supports optional dependencies for various environment families like Atari and MuJoCo.
• Future MuJoCo environments will use mujoco instead of mujoco-py.

Maintenance & Community

Gym is no longer actively maintained; all future development has moved to Gymnasium, a drop-in replacement. Users are strongly advised to migrate.

Licensing & Compatibility

MIT License. Compatible with commercial and closed-source applications.

Limitations & Caveats

Gym is in maintenance mode and will not receive future updates. The project strongly recommends migrating to Gymnasium for ongoing support and development. Windows is not officially supported.