garage.np.exploration_policies.add_ornstein_uhlenbeck_noise
¶
Ornstein-Uhlenbeck exploration strategy.
Ornstein-Uhlenbeck exploration strategy comes from the Ornstein-Uhlenbeck process. It is often used in DDPG algorithm because in continuous control task it is better to have temporally correlated exploration to get smoother transitions. And OU process is relatively smooth in time.
- class AddOrnsteinUhlenbeckNoise(env_spec, policy, *, mu=0, sigma=0.3, theta=0.15, dt=0.01, x0=None)[source]¶
Bases:
garage.np.exploration_policies.exploration_policy.ExplorationPolicy
An exploration strategy based on the Ornstein-Uhlenbeck process.
The process is governed by the following stochastic differential equation.
\[dx_t = -\theta(\mu - x_t)dt + \sigma \sqrt{dt} \mathcal{N}(\mathbb{0}, \mathbb{1}) # noqa: E501\]- Parameters
env_spec (EnvSpec) – Environment to explore.
policy (garage.Policy) – Policy to wrap.
mu (float) – \(\mu\) parameter of this OU process. This is the drift component.
sigma (float) – \(\sigma > 0\) parameter of this OU process. This is the coefficient for the Wiener process component. Must be greater than zero.
theta (float) – \(\theta > 0\) parameter of this OU process. Must be greater than zero.
dt (float) – Time-step quantum \(dt > 0\) of this OU process. Must be greater than zero.
x0 (float) – Initial state \(x_0\) of this OU process.
- reset(dones=None)[source]¶
Reset the state of the exploration.
- Parameters
dones (List[bool] or numpy.ndarray or None) – Which vectorization states to reset.
- get_action(observation)[source]¶
Return an action with noise.
- Parameters
observation (np.ndarray) – Observation from the environment.
- Returns
An action with noise. dict: Arbitrary policy state information (agent_info).
- Return type
np.ndarray
- get_actions(observations)[source]¶
Return actions with noise.
- Parameters
observations (np.ndarray) – Observation from the environment.
- Returns
Actions with noise. List[dict]: Arbitrary policy state information (agent_info).
- Return type
np.ndarray
- update(episode_batch)¶
Update the exploration policy using a batch of trajectories.
- Parameters
episode_batch (EpisodeBatch) – A batch of trajectories which were sampled with this policy active.
- get_param_values()¶
Get parameter values.
- set_param_values(params)¶
Set param values.
- Parameters
params (np.ndarray) – A numpy array of parameter values.