EnvRunner API#

rllib.env.env_runner.EnvRunner#

Construction and setup#

`EnvRunner`	Base class for distributed RL-style data collection from an environment.
`EnvRunner.make_env`	Creates the RL environment for this EnvRunner and assigns it to `self.env`.
`EnvRunner.make_module`	Creates the RLModule for this EnvRunner and assigns it to `self.module`.
`EnvRunner.get_spaces`	Returns a dict mapping ModuleIDs to 2-tuples of obs- and action space.
`EnvRunner.assert_healthy`	Checks that self.__init__() has been completed properly.

Sampling#

`EnvRunner.sample`	Returns experiences (of any form) sampled from this EnvRunner.
`EnvRunner.get_metrics`	Returns metrics (in any form) of the thus far collected, completed episodes.

Cleanup#

EnvRunner.stop

Releases all resources used by this EnvRunner.

Single-agent and multi-agent EnvRunners#

By default, RLlib uses two built-in subclasses of EnvRunner, one for single-agent, one for multi-agent setups. It determines based on your config, which one to use.

Check your config.is_multi_agent property to find out, which of these setups you have configured and see the docs on setting up RLlib multi-agent for more details.