garage.sampler.on_policy_vectorized_sampler module¶
BatchSampler which uses VecEnvExecutor to run multiple environments.
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class
OnPolicyVectorizedSampler
(algo, env, n_envs=None)[source]¶ Bases:
garage.sampler.batch_sampler.BatchSampler
BatchSampler which uses VecEnvExecutor to run multiple environments.
Parameters: - algo (garage.np.algo.RLAlgorithm) – A garage algo object
- env (gym.Env) – A gym/akro env object
- n_envs (int) – Number of parallel environments used for sampling.
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obtain_samples
(itr, batch_size=None, whole_paths=True)[source]¶ Sample the policy for new trajectories.
Parameters: - itr (int) – Iteration number.
- batch_size (int) – Number of samples to be collected. If None, it will be default [algo.max_path_length * n_envs].
- whole_paths (bool) – Whether return all the paths or not. True by default. It’s possible for the paths to have total actual sample size larger than batch_size, and will be truncated if this flag is true.
Returns: - Sample paths, each path with key
- observations: (numpy.ndarray)
- actions: (numpy.ndarray)
- rewards: (numpy.ndarray)
- agent_infos: (dict)
- env_infos: (dict)
Return type: