garage.np.exploration_policies package¶
Exploration strategies which use NumPy as a numerical backend.
-
class
EpsilonGreedyPolicy
(env_spec, policy, *, total_timesteps, max_epsilon=1.0, min_epsilon=0.02, decay_ratio=0.1)[source]¶ Bases:
garage.np.exploration_policies.exploration_policy.ExplorationPolicy
ϵ-greedy exploration strategy.
Select action based on the value of ϵ. ϵ will decrease from max_epsilon to min_epsilon within decay_ratio * total_timesteps.
At state s, with probability 1 − ϵ: select action = argmax Q(s, a) ϵ : select a random action from an uniform distribution.
Parameters: - env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
- policy (garage.Policy) – Policy to wrap.
- total_timesteps (int) – Total steps in the training, equivalent to max_path_length * n_epochs.
- max_epsilon (float) – The maximum(starting) value of epsilon.
- min_epsilon (float) – The minimum(terminal) value of epsilon.
- decay_ratio (float) – Fraction of total steps for epsilon decay.
-
class
ExplorationPolicy
(policy)[source]¶ Bases:
abc.ABC
Policy that wraps another policy to add action noise.
Parameters: policy (garage.Policy) – Policy to wrap. -
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
-
get_param_values
()[source]¶ Get parameter values.
Returns: Values of each parameter. Return type: list or dict
-
-
class
AddGaussianNoise
(env_spec, policy, max_sigma=1.0, min_sigma=0.1, decay_period=1000000)[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.
- 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_period (int) – Number of paths over which to linearly decay sigma from max_sigma to min_sigma.
-
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
-
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.
-
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