ray.rllib.algorithms.algorithm_config.AlgorithmConfig.get_rl_module_spec#

AlgorithmConfig.get_rl_module_spec(env: Any | gymnasium.Env | None = None, spaces: Dict[str, gymnasium.Space] | None = None, inference_only: bool | None = None) → RLModuleSpec[source]#

Returns the RLModuleSpec based on the given env/spaces.

Parameters:

  • env – An optional environment instance, from which to infer the observation- and action spaces for the RLModule. If not provided, tries to infer from spaces, otherwise from self.observation_space and self.action_space. Raises an error, if no information on spaces can be inferred.

  • spaces – Optional dict mapping ModuleIDs to 2-tuples of observation- and action space that should be used for the respective RLModule. These spaces are usually provided by an already instantiated remote EnvRunner (call EnvRunner.get_spaces()). If not provided, tries to infer from env, otherwise from self.observation_space and self.action_space. Raises an error, if no information on spaces can be inferred.

  • inference_only – If True, the returned module spec is used in an inference-only setting (sampling) and the RLModule can thus be built in its light version (if available). For example, the inference_only version of an RLModule might only contain the networks required for computing actions, but misses additional target- or critic networks.

Returns:

A new RLModuleSpec instance that can be used to build an RLModule.