RLenv.directory allows you to explore and find exotic environments.

Learning environments are the datasets of reinforcement learning, a key piece for progress in the field. Our mission is to encourage the creation of new and more complex learning environments by making their discovery easy.

Click here to start browsing :)

Features

• Filter environments by descriptive tags
• Order by number of Github stars
• Filter by complexity and number of agents
• 150+ indexed environments

Contributing

All learning environments are stored in a easy to edit json file, steps for adding a new environment are:

1. Forking the repository
2. Adding the environment to "site/data/envs.json"
3. Opening a pull request

For ideas on different ways you can contribute, head over to the contribution guide, we are waiting for you on the other side!

Bug Reports

All issue reports are welcome, more detailed guidelines will be added soon.