garage.np.exploration_policies.add_ornstein_uhlenbeck_noise module¶
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.ExplorationPolicyAn 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.
-
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