garage.replay_buffer¶
Replay buffers.
The replay buffer primitives can be used for RL algorithms.
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class
HERReplayBuffer(replay_k, reward_fn, capacity_in_transitions, env_spec)¶ Bases:
garage.replay_buffer.path_buffer.PathBuffer
Replay buffer for HER (Hindsight Experience Replay).
It constructs hindsight examples using future strategy.
Parameters: - replay_k (int) – Number of HER transitions to add for each regular Transition. Setting this to 0 means that no HER replays will be added.
- reward_fn (callable) – Function to re-compute the reward with substituted goals.
- capacity_in_transitions (int) – total size of transitions in the buffer.
- env_spec (EnvSpec) – Environment specification.
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n_transitions_stored¶ Return the size of the replay buffer.
Returns: Size of the current replay buffer. Return type: int
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add_path(self, path)¶ Adds a path to the replay buffer.
For each transition in the given path except the last one, replay_k HER transitions will added to the buffer in addition to the one in the path. The last transition is added without sampling additional HER goals.
Parameters: path (dict[str, np.ndarray]) – Each key in the dict must map to a np.ndarray of shape \((T, S^*)\).
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add_episode_batch(self, episodes)¶ Add a EpisodeBatch to the buffer.
Parameters: episodes (EpisodeBatch) – Episodes to add.
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sample_path(self)¶ Sample a single path from the buffer.
Returns: A dict of arrays of shape (path_len, flat_dim). Return type: path
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sample_transitions(self, batch_size)¶ Sample a batch of transitions from the buffer.
Parameters: batch_size (int) – Number of transitions to sample. Returns: A dict of arrays of shape (batch_size, flat_dim). Return type: dict
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clear(self)¶ Clear buffer.
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class
PathBuffer(capacity_in_transitions)¶ A replay buffer that stores and can sample whole episodes.
This buffer only stores valid steps, and doesn’t require paths to have a maximum length.
Parameters: capacity_in_transitions (int) – Total memory allocated for the buffer. -
n_transitions_stored¶ Return the size of the replay buffer.
Returns: Size of the current replay buffer. Return type: int
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add_episode_batch(self, episodes)¶ Add a EpisodeBatch to the buffer.
Parameters: episodes (EpisodeBatch) – Episodes to add.
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add_path(self, path)¶ Add a path to the buffer.
Parameters: path (dict) – A dict of array of shape (path_len, flat_dim). Raises: ValueError– If a key is missing from path or path has wrong shape.
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sample_path(self)¶ Sample a single path from the buffer.
Returns: A dict of arrays of shape (path_len, flat_dim). Return type: path
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sample_transitions(self, batch_size)¶ Sample a batch of transitions from the buffer.
Parameters: batch_size (int) – Number of transitions to sample. Returns: A dict of arrays of shape (batch_size, flat_dim). Return type: dict
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clear(self)¶ Clear buffer.
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class
ReplayBuffer(env_spec, size_in_transitions, time_horizon)¶ Abstract class for Replay Buffer.
Parameters: -
full¶ Whether the buffer is full.
Returns: - True of the buffer has reachd its maximum size.
- False otherwise.
Return type: bool
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n_transitions_stored¶ Return the size of the replay buffer.
Returns: Size of the current replay buffer. Return type: int
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store_episode(self)¶ Add an episode to the buffer.
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sample(self, batch_size)¶ Sample a transition of batch_size.
Parameters: batch_size (int) – The number of transitions to be sampled.
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add_transition(self, **kwargs)¶ Add one transition into the replay buffer.
Parameters: kwargs (dict(str, [numpy.ndarray])) – Dictionary that holds the transitions.
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add_transitions(self, **kwargs)¶ Add multiple transitions into the replay buffer.
A transition contains one or multiple entries, e.g. observation, action, reward, terminal and next_observation. The same entry of all the transitions are stacked, e.g. {‘observation’: [obs1, obs2, obs3]} where obs1 is one numpy.ndarray observation from the environment.
Parameters: kwargs (dict(str, [numpy.ndarray])) – Dictionary that holds the transitions.
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