garage._dtypes
¶
Data types for agent-based learning.
- class StepType¶
Bases:
enum.IntEnum
Defines the status of a
TimeStep
within a sequence.Note that the last
TimeStep
in a sequence can either be :attribute:`StepType.TERMINAL` or :attribute:`StepType.TIMEOUT`.Suppose max_episode_length = 5: * A success sequence terminated at step 4 will look like:
FIRST, MID, MID, TERMINAL
- A success sequence terminated at step 5 will look like:
FIRST, MID, MID, MID, TERMINAL
- An unsuccessful sequence truncated by time limit will look like:
FIRST, MID, MID, MID, TIMEOUT
- class denominator¶
the denominator of a rational number in lowest terms
- class imag¶
the imaginary part of a complex number
- class numerator¶
the numerator of a rational number in lowest terms
- class real¶
the real part of a complex number
- FIRST = 0¶
- MID = 1¶
- TERMINAL = 2¶
- TIMEOUT = 3¶
- classmethod get_step_type(cls, step_cnt, max_episode_length, done)[source]¶
Determines the step type based on step cnt and done signal.
- Parameters
- Returns
the step type.
- Return type
- Raises
ValueError – if step_cnt is < 1. In this case a environment’s
reset()` is likely not called yet and the step_cnt is None –
- bit_length()¶
Number of bits necessary to represent self in binary.
>>> bin(37) '0b100101' >>> (37).bit_length() 6
- conjugate()¶
Returns self, the complex conjugate of any int.
- to_bytes()¶
Return an array of bytes representing an integer.
- length
Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes.
- byteorder
The byte order used to represent the integer. If byteorder is ‘big’, the most significant byte is at the beginning of the byte array. If byteorder is ‘little’, the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder’ as the byte order value.
- signed
Determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.
- name(self)¶
The name of the Enum member.
- value(self)¶
The value of the Enum member.
- class TimeStep[source]¶
A single TimeStep in an environment.
- A
TimeStep
represents a single sample when an agent interacts with an environment. It describes as SARS (State–action–reward–state) tuple that characterizes the evolution of a MDP.
- episode_info¶
A dict of numpy arrays of shape \((S*^,)\) containing episode-level information of each episode. For example, in goal-conditioned reinforcement learning this could contain the goal state for each episode.
- observation¶
A numpy array of shape \((O^*)\) containing the observation for this time step in the environment. These must conform to
EnvStep.observation_space
. The observation before applying the action. None if step_type is StepType.FIRST, i.e. at the start of a sequence.- Type
numpy.ndarray
- action¶
A numpy array of shape \((A^*)\) containing the action for this time step. These must conform to
EnvStep.action_space
. None if step_type is StepType.FIRST, i.e. at the start of a sequence.- Type
numpy.ndarray
- reward¶
A float representing the reward for taking the action given the observation, at this time step. None if step_type is StepType.FIRST, i.e. at the start of a sequence.
- Type
- next_observation¶
A numpy array of shape \((O^*)\) containing the observation for this time step in the environment. These must conform to
EnvStep.observation_space
. The observation after applying the action.- Type
numpy.ndarray
- agent_info¶
A dict of arbitrary agent state information. For example, this may contain the hidden states from an RNN policy.
- Type
- step_type¶
a
StepType
enum value. Can be one of :attribute:`~StepType.FIRST`, :attribute:`~StepType.MID`, :attribute:`~StepType.TERMINAL`, or :attribute:`~StepType.TIMEOUT`.- Type
- env_spec :garage.EnvSpec¶
- episode_info :Dict[str, numpy.ndarray]¶
- observation :numpy.ndarray¶
- action :numpy.ndarray¶
- reward :float¶
- next_observation :numpy.ndarray¶
- env_info :Dict[str, numpy.ndarray]¶
- agent_info :Dict[str, numpy.ndarray]¶
- step_type :StepType¶
- property first(self)¶
bool: Whether this step is the first of its episode.
- property mid(self)¶
bool: Whether this step is in the middle of its episode.
- property terminal(self)¶
bool: Whether this step records a termination condition.
- property timeout(self)¶
bool: Whether this step records a timeout condition.
- property last(self)¶
bool: Whether this step is the last of its episode.
- classmethod from_env_step(cls, env_step, last_observation, agent_info, episode_info)[source]¶
Create a TimeStep from a EnvStep.
- Parameters
env_step (EnvStep) – the env step returned by the environment.
last_observation (numpy.ndarray) – A numpy array of shape \((O^*)\) containing the observation for this time step in the environment. These must conform to
EnvStep.observation_space
. The observation before applying the action.agent_info (dict) – A dict of arbitrary agent state information.
episode_info (dict) – A dict of arbitrary information associated with the whole episode.
- Returns
The TimeStep with all information of EnvStep plus the agent info.
- Return type
- A
- class TimeStepBatch[source]¶
A tuple representing a batch of TimeSteps.
Data type for off-policy algorithms, imitation learning and batch-RL.
- episode_infos¶
A dict of numpy arrays containing the episode-level information of each episode. Each value of this dict should be a numpy array of shape \((N, S^*)\). For example, in goal-conditioned reinforcement learning this could contain the goal state for each episode.
- observations¶
Non-flattened array of observations. Typically has shape (batch_size, S^*) (the unflattened state space of the current environment).
- Type
numpy.ndarray
- actions¶
Non-flattened array of actions. Must have shape (batch_size, S^*) (the unflattened action space of the current environment).
- Type
numpy.ndarray
- rewards¶
Array of rewards of shape (batch_size, 1).
- Type
numpy.ndarray
- next_observation¶
Non-flattened array of next observations. Has shape (batch_size, S^*). next_observations[i] was observed by the agent after taking actions[i].
- Type
numpy.ndarray
- agent_infos¶
A dict of arbitrary agent state information. For example, this may contain the hidden states from an RNN policy.
- Type
- step_types¶
A numpy array of `StepType with shape ( batch_size,) containing the time step types for all transitions in this batch.
- Type
numpy.ndarray
- Raises
ValueError – If any of the above attributes do not conform to their prescribed types and shapes.
- env_spec :garage.EnvSpec¶
- episode_infos :Dict[str, np.ndarray or dict]¶
- observations :numpy.ndarray¶
- actions :numpy.ndarray¶
- rewards :numpy.ndarray¶
- next_observations :numpy.ndarray¶
- agent_infos :Dict[str, np.ndarray or dict]¶
- env_infos :Dict[str, np.ndarray or dict]¶
- step_types :numpy.ndarray¶
- classmethod concatenate(cls, *batches)[source]¶
Concatenate two or more :class:`TimeStepBatch`s.
- Parameters
batches (list[TimeStepBatch]) – Batches to concatenate.
- Returns
The concatenation of the batches.
- Return type
- Raises
ValueError – If no TimeStepBatches are provided.
- split(self) List[TimeStepBatch] [source]¶
Split a
TimeStepBatch
into a list of :class:`~TimeStepBatch`s.The opposite of concatenate.
- Returns
- A list of :class:`TimeStepBatch`s, with one
TimeStep
perTimeStepBatch
.
- Return type
- to_time_step_list(self) List[Dict[str, numpy.ndarray]] [source]¶
Convert the batch into a list of dictionaries.
Breaks the
TimeStepBatch
into a list of single time step sample dictionaries. len(rewards) (or the number of discrete time step) dictionaries are returned- Returns
- Keys:
- episode_infos (dict[str, np.ndarray]): A dict of numpy arrays
containing the episode-level information of each episode. Each value of this dict must be a numpy array of shape \((S^*,)\). For example, in goal-conditioned reinforcement learning this could contain the goal state for each episode.
- observations (numpy.ndarray): Non-flattened array of
observations. Typically has shape (batch_size, S^*) (the unflattened state space of the current environment).
- actions (numpy.ndarray): Non-flattened array of actions. Must
have shape (batch_size, S^*) (the unflattened action space of the current environment).
- rewards (numpy.ndarray): Array of rewards of shape (
batch_size,) (1D array of length batch_size).
- next_observation (numpy.ndarray): Non-flattened array of next
observations. Has shape (batch_size, S^*). next_observations[i] was observed by the agent after taking actions[i].
- env_infos (dict): A dict arbitrary environment state
information.
- agent_infos (dict): A dict of arbitrary agent state
information. For example, this may contain the hidden states from an RNN policy.
- step_types (numpy.ndarray): A numpy array of `StepType with
shape (batch_size,) containing the time step types for all transitions in this batch.
- Return type
- property terminals(self)¶
Get an array of boolean indicating ternianal information.
- classmethod from_time_step_list(cls, env_spec, ts_samples)[source]¶
Create a
TimeStepBatch
from a list of time step dictionaries.- Parameters
env_spec (EnvSpec) – Specification for the environment from which this data was sampled.
ts_samples (list[dict[str, np.ndarray or dict[str, np.ndarray]]]) –
keys: * episode_infos (dict[str, np.ndarray]): A dict of numpy arrays
containing the episode-level information of each episode. Each value of this dict must be a numpy array of shape \((N, S^*)\). For example, in goal-conditioned reinforcement learning this could contain the goal state for each episode.
- observations (numpy.ndarray): Non-flattened array of
observations. Typically has shape (batch_size, S^*) (the unflattened state space of the current environment).
- actions (numpy.ndarray): Non-flattened array of actions.
Must have shape (batch_size, S^*) (the unflattened action space of the current environment).
- rewards (numpy.ndarray): Array of rewards of shape (
batch_size,) (1D array of length batch_size).
- next_observation (numpy.ndarray): Non-flattened array of next
observations. Has shape (batch_size, S^*). next_observations[i] was observed by the agent after taking actions[i].
- env_infos (dict): A dict arbitrary environment state
information.
- agent_infos (dict): A dict of arbitrary agent
state information. For example, this may contain the hidden states from an RNN policy.
step_types (numpy.ndarray): A numpy array of `StepType with
- shape (batch_size,) containing the time step types for all
transitions in this batch.
- Returns
The concatenation of samples.
- Return type
- Raises
ValueError – If no dicts are provided.
- class EpisodeBatch(env_spec, episode_infos, observations, last_observations, actions, rewards, env_infos, agent_infos, step_types, lengths)[source]¶
Bases:
TimeStepBatch
A tuple representing a batch of whole episodes.
Data type for on-policy algorithms.
A
EpisodeBatch
represents a batch of whole episodes, produced when one or more agents interacts with one or more environments.Symbol
Description
\(N\)
Episode batch dimension
\([T]\)
Variable-length time dimension of each episode
\(S^*\)
Single-step shape of a time-series tensor
\(N \bullet [T]\)
A dimension computed by flattening a variable-length time dimension \([T]\) into a single batch dimension with length \(sum_{i \in N} [T]_i\)
- episode_infos¶
A dict of numpy arrays containing the episode-level information of each episode. Each value of this dict should be a numpy array of shape \((N, S^*)\). For example, in goal-conditioned reinforcement learning this could contain the goal state for each episode.
- observations¶
A numpy array of shape \((N \bullet [T], O^*)\) containing the (possibly multi-dimensional) observations for all time steps in this batch. These must conform to
EnvStep.observation_space
.- Type
numpy.ndarray
- last_observations¶
A numpy array of shape \((N, O^*)\) containing the last observation of each episode. This is necessary since there are one more observations than actions every episode.
- Type
numpy.ndarray
- actions¶
A numpy array of shape \((N \bullet [T], A^*)\) containing the (possibly multi-dimensional) actions for all time steps in this batch. These must conform to
EnvStep.action_space
.- Type
numpy.ndarray
- rewards¶
A numpy array of shape \((N \bullet [T])\) containing the rewards for all time steps in this batch.
- Type
numpy.ndarray
- env_infos¶
A dict of numpy arrays arbitrary environment state information. Each value of this dict should be a numpy array of shape \((N \bullet [T])\) or \((N \bullet [T], S^*)\).
- agent_infos¶
A dict of numpy arrays arbitrary agent state information. Each value of this dict should be a numpy array of shape \((N \bullet [T])\) or \((N \bullet [T], S^*)\). For example, this may contain the hidden states from an RNN policy.
- step_types¶
A numpy array of StepType with shape :math:`(N bullet [T]) containing the time step types for all transitions in this batch.
- Type
numpy.ndarray
- lengths¶
An integer numpy array of shape \((N,)\) containing the length of each episode in this batch. This may be used to reconstruct the individual episodes.
- Type
numpy.ndarray
- Raises
ValueError – If any of the above attributes do not conform to their prescribed types and shapes.
- episode_infos_by_episode :numpy.ndarray¶
- last_observations :numpy.ndarray¶
- lengths :numpy.ndarray¶
- env_spec :garage.EnvSpec¶
- observations :numpy.ndarray¶
- actions :numpy.ndarray¶
- rewards :numpy.ndarray¶
- agent_infos :Dict[str, np.ndarray or dict]¶
- env_infos :Dict[str, np.ndarray or dict]¶
- step_types :numpy.ndarray¶
- classmethod concatenate(cls, *batches)[source]¶
Create a EpisodeBatch by concatenating EpisodeBatches.
- Parameters
batches (list[EpisodeBatch]) – Batches to concatenate.
- Returns
The concatenation of the batches.
- Return type
- split(self)[source]¶
Split an EpisodeBatch into a list of EpisodeBatches.
The opposite of concatenate.
- Returns
- A list of EpisodeBatches, with one
episode per batch.
- Return type
- to_list(self)[source]¶
Convert the batch into a list of dictionaries.
- Returns
- Keys:
- observations (np.ndarray): Non-flattened array of
observations. Has shape (T, S^*) (the unflattened state space of the current environment). observations[i] was used by the agent to choose actions[i].
- next_observations (np.ndarray): Non-flattened array of
observations. Has shape (T, S^*). next_observations[i] was observed by the agent after taking actions[i].
- actions (np.ndarray): Non-flattened array of actions. Must
have shape (T, S^*) (the unflattened action space of the current environment).
- rewards (np.ndarray): Array of rewards of shape (T,) (1D
array of length timesteps).
- agent_infos (dict[str, np.ndarray]): Dictionary of stacked,
non-flattened agent_info arrays.
- env_infos (dict[str, np.ndarray]): Dictionary of stacked,
non-flattened env_info arrays.
- step_types (numpy.ndarray): A numpy array of `StepType with
shape (T,) containing the time step types for all transitions in this batch.
- episode_infos (dict[str, np.ndarray]): Dictionary of stacked,
non-flattened episode_info arrays.
- Return type
- classmethod from_list(cls, env_spec, paths)[source]¶
Create a EpisodeBatch from a list of episodes.
- Parameters
env_spec (EnvSpec) – Specification for the environment from which this data was sampled.
paths (list[dict[str, np.ndarray or dict[str, np.ndarray]]]) –
Keys: * episode_infos (dict[str, np.ndarray]): Dictionary of stacked,
non-flattened episode_info arrays, each of shape (S^*).
- observations (np.ndarray): Non-flattened array of
observations. Typically has shape (T, S^*) (the unflattened state space of the current environment). observations[i] was used by the agent to choose actions[i]. observations may instead have shape (T + 1, S^*).
- next_observations (np.ndarray): Non-flattened array of
observations. Has shape (T, S^*). next_observations[i] was observed by the agent after taking actions[i]. Optional. Note that to ensure all information from the environment was preserved, observations[i] must have shape (T + 1, S^*), or this key must be set. However, this method is lenient and will “duplicate” the last observation if the original last observation has been lost.
- actions (np.ndarray): Non-flattened array of actions. Must
have shape (T, S^*) (the unflattened action space of the current environment).
- rewards (np.ndarray): Array of rewards of shape (T,) (1D
array of length timesteps).
- agent_infos (dict[str, np.ndarray]): Dictionary of stacked,
non-flattened agent_info arrays.
- env_infos (dict[str, np.ndarray]): Dictionary of stacked,
non-flattened env_info arrays.
- step_types (numpy.ndarray): A numpy array of `StepType with
shape (T,) containing the time step types for all transitions in this batch.
- property next_observations(self)¶
Get the observations seen after actions are performed.
In an
EpisodeBatch
, next_observations don’t need to be stored explicitly, since the next observation is already stored in the batch.- Returns
- The “next_observations” with shape
\((N \bullet [T], O^*)\)
- Return type
np.ndarray
- property episode_infos(self)¶
Get the episode_infos.
In an
EpisodeBatch
, episode_infos only need to be stored once per episode. However, the episode_infos field ofTimeStepBatch
has shape \((N \bullet [T])\). This method expands episode_infos_by_episode (which have shape \((N)\)) to \((N \bullet [T])\).
- property padded_observations(self)¶
Padded observations.
- Returns
- Padded observations with shape of
\((N, max_episode_length, O^*)\).
- Return type
np.ndarray
- property padded_actions(self)¶
Padded actions.
- Returns
- Padded actions with shape of
\((N, max_episode_length, A^*)\).
- Return type
np.ndarray
- property observations_list(self)¶
Split observations into a list.
- Returns
Splitted list.
- Return type
list[np.ndarray]
- property actions_list(self)¶
Split actions into a list.
- Returns
Splitted list.
- Return type
list[np.ndarray]
- property padded_rewards(self)¶
Padded rewards.
- Returns
- Padded rewards with shape of
\((N, max_episode_length)\).
- Return type
np.ndarray
- property valids(self)¶
An array indicating valid steps in a padded tensor.
- Returns
the shape is \((N, max_episode_length)\).
- Return type
np.ndarray
- property padded_next_observations(self)¶
Padded next_observations array.
- Returns
Array of shape \((N, max_episode_length, O^*)\)
- Return type
np.ndarray
- property padded_step_types(self)¶
Padded step_type array.
- Returns
Array of shape \((N, max_episode_length)\)
- Return type
np.ndarray
- property padded_agent_infos(self)¶
Padded agent infos.
- property padded_env_infos(self)¶
Padded env infos.
- to_time_step_list(self) List[Dict[str, numpy.ndarray]] ¶
Convert the batch into a list of dictionaries.
Breaks the
TimeStepBatch
into a list of single time step sample dictionaries. len(rewards) (or the number of discrete time step) dictionaries are returned- Returns
- Keys:
- episode_infos (dict[str, np.ndarray]): A dict of numpy arrays
containing the episode-level information of each episode. Each value of this dict must be a numpy array of shape \((S^*,)\). For example, in goal-conditioned reinforcement learning this could contain the goal state for each episode.
- observations (numpy.ndarray): Non-flattened array of
observations. Typically has shape (batch_size, S^*) (the unflattened state space of the current environment).
- actions (numpy.ndarray): Non-flattened array of actions. Must
have shape (batch_size, S^*) (the unflattened action space of the current environment).
- rewards (numpy.ndarray): Array of rewards of shape (
batch_size,) (1D array of length batch_size).
- next_observation (numpy.ndarray): Non-flattened array of next
observations. Has shape (batch_size, S^*). next_observations[i] was observed by the agent after taking actions[i].
- env_infos (dict): A dict arbitrary environment state
information.
- agent_infos (dict): A dict of arbitrary agent state
information. For example, this may contain the hidden states from an RNN policy.
- step_types (numpy.ndarray): A numpy array of `StepType with
shape (batch_size,) containing the time step types for all transitions in this batch.
- Return type
- property terminals(self)¶
Get an array of boolean indicating ternianal information.
- classmethod from_time_step_list(cls, env_spec, ts_samples)¶
Create a
TimeStepBatch
from a list of time step dictionaries.- Parameters
env_spec (EnvSpec) – Specification for the environment from which this data was sampled.
ts_samples (list[dict[str, np.ndarray or dict[str, np.ndarray]]]) –
keys: * episode_infos (dict[str, np.ndarray]): A dict of numpy arrays
containing the episode-level information of each episode. Each value of this dict must be a numpy array of shape \((N, S^*)\). For example, in goal-conditioned reinforcement learning this could contain the goal state for each episode.
- observations (numpy.ndarray): Non-flattened array of
observations. Typically has shape (batch_size, S^*) (the unflattened state space of the current environment).
- actions (numpy.ndarray): Non-flattened array of actions.
Must have shape (batch_size, S^*) (the unflattened action space of the current environment).
- rewards (numpy.ndarray): Array of rewards of shape (
batch_size,) (1D array of length batch_size).
- next_observation (numpy.ndarray): Non-flattened array of next
observations. Has shape (batch_size, S^*). next_observations[i] was observed by the agent after taking actions[i].
- env_infos (dict): A dict arbitrary environment state
information.
- agent_infos (dict): A dict of arbitrary agent
state information. For example, this may contain the hidden states from an RNN policy.
step_types (numpy.ndarray): A numpy array of `StepType with
- shape (batch_size,) containing the time step types for all
transitions in this batch.
- Returns
The concatenation of samples.
- Return type
- Raises
ValueError – If no dicts are provided.
- check_timestep_batch(batch, array_type, ignored_fields=())[source]¶
Check a TimeStepBatch of any array type that has .shape.
- Parameters
batch (TimeStepBatch) – Batch of timesteps.
array_type (type) – Array type.
ignored_fields (set[str]) – Set of fields to ignore checking on.
- Raises
ValueError – If an invariant of TimeStepBatch is broken.