garage.np.exploration_policies.add_gaussian_noise¶
Gaussian exploration strategy.
- class AddGaussianNoise(env_spec, policy, total_timesteps, max_sigma=1.0, min_sigma=0.1, decay_ratio=1.0)[source]¶
Bases:
garage.np.exploration_policies.exploration_policy.ExplorationPolicy
Add Gaussian noise to the action taken by the deterministic policy.
- Parameters
env_spec (EnvSpec) – Environment spec to explore.
policy (garage.Policy) – Policy to wrap.
total_timesteps (int) – Total steps in the training, equivalent to max_episode_length * n_epochs.
max_sigma (float) – Action noise standard deviation at the start of exploration.
min_sigma (float) – Action noise standard deviation at the end of the decay period.
decay_ratio (float) – Fraction of total steps for epsilon decay.
- get_action(observation)[source]¶
Get action from this policy for the input observation.
- Parameters
observation (numpy.ndarray) – Observation from the environment.
- Returns
Actions with noise. List[dict]: Arbitrary policy state information (agent_info).
- Return type
np.ndarray
- get_actions(observations)[source]¶
Get actions from this policy for the input observation.
- Parameters
observations (list) – Observations from the environment.
- Returns
Actions with noise. List[dict]: Arbitrary policy state information (agent_info).
- Return type
np.ndarray
- update(episode_batch)[source]¶
Update the exploration policy using a batch of trajectories.
- Parameters
episode_batch (EpisodeBatch) – A batch of trajectories which were sampled with this policy active.