ray.rllib.utils.exploration.exploration.Exploration

class ray.rllib.utils.exploration.exploration.Exploration(action_space: gymnasium.spaces.Space, *, framework: str, policy_config: dict, model: ModelV2, num_workers: int, worker_index: int)

Implements an exploration strategy for Policies.

An Exploration takes model outputs, a distribution, and a timestep from the agent and computes an action to apply to the environment using an implemented exploration schema.

Methods

__init__	param action_space: The action space in which to explore.
before_compute_actions	Hook for preparations before policy.compute_actions() is called.
get_exploration_action	Returns a (possibly) exploratory action and its log-likelihood.
get_exploration_optimizer	May add optimizer(s) to the Policy's own optimizers.
get_state	Returns the current exploration state.
on_episode_end	Handles necessary exploration logic at the end of an episode.
on_episode_start	Handles necessary exploration logic at the beginning of an episode.
postprocess_trajectory	Handles post-processing of done episode trajectories.
set_state	Sets the Exploration object's state to the given values.