garage.np.exploration_policies.epsilon_greedy_policy¶
ϵ-greedy exploration strategy.
Random exploration according to the value of epsilon.
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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_episode_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.
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get_action(self, observation)[source]¶ Get action from this policy for the input observation.
- Parameters
observation (numpy.ndarray) – Observation from the environment.
- Returns
An action with noise. dict: Arbitrary policy state information (agent_info).
- Return type
np.ndarray
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get_actions(self, observations)[source]¶ Get actions from this policy for the input observations.
- Parameters
observations (numpy.ndarray) – Observation from the environment.
- Returns
Actions with noise. List[dict]: Arbitrary policy state information (agent_info).
- Return type
np.ndarray
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update(self, 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.