garage.sampler.utils module¶
Utility functions related to sampling.
-
rollout
(env, agent, *, max_path_length=inf, animated=False, speedup=1, deterministic=False)[source]¶ Sample a single rollout of the agent in the environment.
Parameters: - agent (Policy) – Agent used to select actions.
- env (gym.Env) – Environment to perform actions in.
- max_path_length (int) – If the rollout reaches this many timesteps, it is terminated.
- animated (bool) – If true, render the environment after each step.
- speedup (float) – Factor by which to decrease the wait time between rendered steps. Only relevant, if animated == true.
- deterministic (bool) – If true, use the mean action returned by the stochastic policy instead of sampling from the returned action distribution.
Returns: - Dictionary, with keys:
- observations(np.array): Flattened array of observations.
- There should be one more of these than actions. Note that observations[i] (for i < len(observations) - 1) was used by the agent to choose actions[i]. Should have shape (T + 1, S^*) (the unflattened state space of the current environment).
- actions(np.array): Non-flattened array of actions. Should have
- shape (T, S^*) (the unflattened action space of the current environment).
- rewards(np.array): Array of rewards of shape (T,) (1D array of
- length timesteps).
- agent_infos(Dict[str, np.array]): Dictionary of stacked,
- non-flattened agent_info arrays.
- env_infos(Dict[str, np.array]): Dictionary of stacked,
- non-flattened env_info arrays.
- dones(np.array): Array of termination signals.
Return type:
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truncate_paths
(paths, max_samples)[source]¶ Truncate the paths so that the total number of samples is max_samples.
This is done by removing extra paths at the end of the list, and make the last path shorter if necessary
Parameters: - paths (list[dict[str, np.ndarray]]) – Samples, items with keys: * observations (np.ndarray): Enviroment observations * actions (np.ndarray): Agent actions * rewards (np.ndarray): Environment rewards * env_infos (dict): Environment state information * agent_infos (dict): Agent state information
- max_samples (int) – Maximum number of samples allowed.
Returns: - A list of paths, truncated so that the
number of samples adds up to max-samples
Return type: Raises: ValueError
– If key a other than ‘observations’, ‘actions’, ‘rewards’, ‘env_infos’ and ‘agent_infos’ is found.