Gym is a toolkit from OpenAI that offers a wide array of simulated environments (e.g. Atari games, board games, 2D and 3D physical simulations) for you to train agents, benchmark them, and create new Reinforcement Learning algorithms.
| General | |
|---|---|
| Relese date | April 27, 2016 |
| Author | OpenAI |
| Repository | https://github.com/openai/gym |
| Type | Reinforcement Learning |
Libraries
A curated list of libraries and technologies to help you play with OpenAI Gym.
- Gym Library Gym is a standard API for reinforcement learning, and a diverse collection of reference environments
- OpenAI Gym repository Gym is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Since its release, Gym's API has become the field standard for doing this.
- Gymnasium Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. This is a fork of OpenAI's Gym library
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