.. include:: /_includes/rllib/we_are_hiring.rst

.. _env-reference-docs:

Environments

.. include:: /_includes/rllib/new_api_stack.rst

RLlib mainly supports the Farama gymnasium API <https://gymnasium.farama.org/>__ for single-agent environments, and RLlib's own :py:class:~ray.rllib.env.multi_agent_env.MultiAgentEnv API for multi-agent setups.

Env Vectorization

For single-agent setups, RLlib automatically vectorizes your provided gymnasium.Env <https://gymnasium.farama.org/_modules/gymnasium/core/#Env>__ using gymnasium's own vectorization feature <https://gymnasium.farama.org/api/vector/>__.

Use the config.env_runners(num_envs_per_env_runner=..) setting to vectorize your env beyond 1 env copy.

External Envs

.. note::

External Env support is under development on the new API stack. The recommended
way to implement your own external env connection logic, for example through TCP or
shared memory, is to write your own :py:class:`~ray.rllib.env.env_runner.EnvRunner`
subclass.

See this an end-to-end example of an external CartPole (client) env <https://github.com/ray-project/ray/blob/master/rllib/examples/envs/env_connecting_to_rllib_w_tcp_client.py>__ connecting to RLlib through a custom, TCP-capable :py:class:~ray.rllib.env.env_runner.EnvRunner server.

Environment API Reference

.. toctree:: :maxdepth: 1

env/env_runner.rst env/single_agent_env_runner.rst env/single_agent_episode.rst env/multi_agent_env.rst env/multi_agent_env_runner.rst env/multi_agent_episode.rst env/external.rst env/utils.rst