garage._dtypes

Data types for agent-based learning.

class EpisodeBatch

Bases: collections.namedtuple()

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 \(sum_{i \in N} [T]_i\)

env_spec

Specification for the environment from which this data was sampled.

Type:EnvSpec

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 \((N \bullet [T], S^*)\).

Type:dict

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.

Type:numpy.ndarray

step_types

A numpy array of StepType with shape :math:`(N,) 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.

next_observations

Get the observations seen after actions are performed.

Usually, 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”. Return type:np.ndarray

classmethod concatenate(cls, *batches)

Create a EpisodeBatch by concatenating EpisodeBatches.

Parameters:batches (list[EpisodeBatch]) – Batches to concatenate. Returns:The concatenation of the batches. Return type:EpisodeBatch

split(self)

Split an EpisodeBatch into a list of EpisodeBatches.

The opposite of concatenate.

Returns:

A list of EpisodeBatches, with one

episode per batch.

Return type:list[EpisodeBatch]

to_list(self)

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. Should

  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.

Return type:list[dict[str, np.ndarray or dict[str, np.ndarray]]]

classmethod from_list(cls, env_spec, paths)

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: * 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] should have shape (T + 1, S^*), or this key should 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. Should

    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.

count()

Return number of occurrences of value.

index()

Return first index of value.

Raises ValueError if the value is not present.

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)

Determines the step type based on step cnt and done signal.

Parameters:

• step_cnt (int) – current step cnt of the environment.
• max_episode_length (int) – maximum episode length.
• done (bool) – the done signal returned by Environment.

Returns:

the step type.

Return type:

StepType

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

Bases: collections.namedtuple()

A tuple representing a single TimeStep.

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.

env_spec

Specification for the environment from which this data was sampled.

Type:EnvSpec

observation

A numpy array of shape \((O^*)\) containing the observation for the 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 the 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 the this time step. None if step_type is StepType.FIRST, i.e. at the start of a sequence.

Type:float

next_observation

A numpy array of shape \((O^*)\) containing the observation for the this time step in the environment. These must conform to EnvStep.observation_space. The observation after applying the action.

Type:numpy.ndarray

env_info

A dict arbitrary environment state information.

Type:dict

agent_info

A dict of arbitrary agent state information. For example, this may contain the hidden states from an RNN policy.

Type:dict

step_type

a StepType enum value. Can be one of :attribute:`~StepType.FIRST`, :attribute:`~StepType.MID`, :attribute:`~StepType.TERMINAL`, or :attribute:`~StepType.TIMEOUT`.

Type:StepType

first

Whether this step is the first of its episode.

Type:bool

mid

Whether this step is in the middle of its episode.

Type:bool

terminal

Whether this step records a termination condition.

Type:bool

timeout

Whether this step records a timeout condition.

Type:bool

last

Whether this step is the last of its episode.

Type:bool

classmethod from_env_step(cls, env_step, last_observation, agent_info)

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 the 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.

Returns:

The TimeStep with all information of EnvStep plus the agent info.

Return type:

TimeStep

count()

Return number of occurrences of value.

index()

Return first index of value.

Raises ValueError if the value is not present.

class InOutSpec(input_space, output_space)

Describes the input and output spaces of a primitive or module.

Parameters:

• input_space (akro.Space) – Input space of a module.
• output_space (akro.Space) – Output space of a module.

input_space

Get input space of the module.

Returns:Input space of the module. Return type:akro.Space

output_space

Get output space of the module.

Returns:Output space of the module. Return type:akro.Space

class TimeStepBatch

Bases: collections.namedtuple()

A tuple representing a batch of TimeSteps.

Data type for off-policy algorithms, imitation learning and batch-RL.

env_spec

Specification for the environment from which this data was sampled.

Type:EnvSpec

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. Should have shape (batch_size, S^*) (the unflattened action space of the current environment).

Type:numpy.ndarray

rewards

Array of rewards of shape (batch_size,) (1D array of length batch_size).

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

env_infos

A dict arbitrary environment state information.

Type:dict

agent_infos

A dict of arbitrary agent state information. For example, this may contain the hidden states from an RNN policy.

Type:dict

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.

classmethod concatenate(cls, *batches)

Concatenate two or more :class:`TimeStepBatch`s.

Parameters:batches (list[TimeStepBatch]) – Batches to concatenate. Returns:The concatenation of the batches. Return type:TimeStepBatch Raises:ValueError – If no TimeStepBatches are provided.

split(self)

Split a TimeStepBatch into a list of :class:`~TimeStepBatch`s.

The opposite of concatenate.

Returns:

A list of :class:`TimeStepBatch`s, with one

TimeStep per TimeStepBatch.

Return type:list[TimeStepBatch]

to_time_step_list(self)

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:

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. Should

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:list[dict[str, np.ndarray or dict[str, np.ndarray]]]

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: * 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.

    Should 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:

TimeStepBatch

Raises:

ValueError – If no dicts are provided.

classmethod from_episode_batch(cls, batch)

Construct a TimeStepBatch from an EpisodeBatch.

Parameters:batch (EpisodeBatch) – Episode batch to convert. Returns:The converted batch. Return type:TimeStepBatch

count()

Return number of occurrences of value.

index()

Return first index of value.

Raises ValueError if the value is not present.