ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling#

class ray.rllib.utils.exploration.stochastic_sampling.StochasticSampling(action_space: <MagicMock name='mock.spaces.Space' id='140494124151808'>, *, framework: str, model: ray.rllib.models.modelv2.ModelV2, random_timesteps: int = 0, **kwargs)[source]#

Bases: ray.rllib.utils.exploration.exploration.Exploration

An exploration that simply samples from a distribution.

The sampling can be made deterministic by passing explore=False into the call to get_exploration_action. Also allows for scheduled parameters for the distributions, such as lowering stddev, temperature, etc.. over time.

Methods

__init__(action_space, *, framework, model)

Initializes a StochasticSampling Exploration object.

before_compute_actions(*[, timestep, ...])

Hook for preparations before policy.compute_actions() is called.

get_exploration_optimizer(optimizers)

May add optimizer(s) to the Policy's own optimizers.

get_state([sess])

Returns the current exploration state.

on_episode_end(policy, *[, environment, ...])

Handles necessary exploration logic at the end of an episode.

on_episode_start(policy, *[, environment, ...])

Handles necessary exploration logic at the beginning of an episode.

postprocess_trajectory(policy, sample_batch)

Handles post-processing of done episode trajectories.

set_state(state[, sess])

Sets the Exploration object's state to the given values.