`garage`¶

Garage Base.

class EpisodeBatch(env_spec, episode_infos, observations, last_observations, actions, rewards, env_infos, agent_infos, step_types, lengths)¶

Bases: TimeStepBatch

Inheritance diagram of garage.EpisodeBatch

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

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.

Type: dict[str, np.ndarray]

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[str, np.ndarray]

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: dict[str, np.ndarray]

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.

property next_observations¶

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¶

Get the episode_infos.

In an EpisodeBatch, episode_infos only need to be stored once per episode. However, the episode_infos field of TimeStepBatch has shape \((N \bullet [T])\). This method expands episode_infos_by_episode (which have shape \((N)\)) to \((N \bullet [T])\).

Returns

The episode_infos each of length :math:`(N: bullet [T])`.

Return type

dict[str, np.ndarray]

property padded_observations¶

Padded observations.

Returns

Padded observations with shape of: \((N, max_episode_length, O^*)\).

Return type

np.ndarray

property padded_actions¶

Padded actions.

Returns

Padded actions with shape of: \((N, max_episode_length, A^*)\).

Return type

np.ndarray

property observations_list¶

Split observations into a list.

Returns: Splitted list.
Return type: list[np.ndarray]

property actions_list¶

Split actions into a list.

Returns: Splitted list.
Return type: list[np.ndarray]

property padded_rewards¶

Padded rewards.

Returns

Padded rewards with shape of: \((N, max_episode_length)\).

Return type

np.ndarray

property valids¶

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¶

Padded next_observations array.

Returns: Array of shape \((N, max_episode_length, O^*)\)
Return type: np.ndarray

property padded_step_types¶

Padded step_type array.

Returns: Array of shape \((N, max_episode_length)\)
Return type: np.ndarray

property padded_agent_infos¶

Padded agent infos.

Returns

Padded agent infos. Each value must have: shape with \((N, max_episode_length)\) or \((N, max_episode_length, S^*)\).

Return type

dict[str, np.ndarray]

property padded_env_infos¶

Padded env infos.

Returns

Padded env infos. Each value must have: shape with \((N, max_episode_length)\) or \((N, max_episode_length, S^*)\).

Return type

dict[str, np.ndarray]

property terminals¶

Get an array of boolean indicating ternianal information.

Returns

An array of boolean of shape \((N,)\): indicating whether the `StepType is `TERMINAL

Return type

numpy.ndarray

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(*batches)¶

Create a EpisodeBatch by concatenating EpisodeBatches.

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

split()¶

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()¶

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

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

classmethod from_list(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: * 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.

to_time_step_list() → 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

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

classmethod from_time_step_list(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

TimeStepBatch

Raises

ValueError – If no dicts are provided.

class TimeStep¶

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.

env_spec¶

Specification for the environment from which this data was sampled.

Type: EnvSpec

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.

Type: dict[str, np.ndarray]

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: float

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

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

property first¶

Whether this step is the first of its episode.

Type: bool

property mid¶

Whether this step is in the middle of its episode.

Type: bool

property terminal¶

Whether this step records a termination condition.

Type: bool

property timeout¶

Whether this step records a timeout condition.

Type: bool

property last¶

Whether this step is the last of its episode.

Type: bool

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¶

classmethod from_env_step(env_step, last_observation, agent_info, episode_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 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

TimeStep

class TimeStepBatch¶

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

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.

Type: dict[str, np.ndarray]

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

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.

property terminals¶

Get an array of boolean indicating ternianal information.

Returns

An array of boolean of shape \((N,)\): indicating whether the `StepType is `TERMINAL

Return type

numpy.ndarray

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(*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() → List[TimeStepBatch]¶

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() → 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

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

classmethod from_time_step_list(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

TimeStepBatch

Raises

ValueError – If no dicts are provided.

class Environment¶

Bases: abc.ABC

Inheritance diagram of garage.Environment

The main API for garage environments.

The public API methods are:

Functions
reset()
step()
render()
visualize()
close()

Set the following properties:

Properties	Description
action_space	The action space specification
observation_space	The observation space specification
spec	The environment specifications
render_modes	The list of supported render modes

Example of a simple rollout loop:

env = MyEnv()
policy = MyPolicy()
first_observation, episode_info = env.reset()
env.visualize()  # visualization window opened

episode = []
# Determine the first action
first_action = policy.get_action(first_observation, episode_info)
episode.append(env.step(first_action))

while not episode[-1].last():
   action = policy.get_action(episode[-1].observation)
   episode.append(env.step(action))

env.close()  # visualization window closed

Make sure your environment is pickle-able:

Garage pickles the environment via the cloudpickle module to save snapshots of the experiment. However, some environments may contain attributes that are not pickle-able (e.g. a client-server connection). In such cases, override __setstate__() and __getstate__() to add your custom pickle logic.

You might want to refer to the EzPickle module: https://github.com/openai/gym/blob/master/gym/utils/ezpickle.py for a lightweight way of pickle and unpickle via constructor arguments.

abstract property action_space¶

The action space specification.

Type: akro.Space

abstract property observation_space¶

The observation space specification.

Type: akro.Space

abstract property spec¶

The environment specification.

Type: EnvSpec

abstract property render_modes¶

A list of string representing the supported render modes.

See render() for a list of modes.

Type: list

abstract reset()¶

Resets the environment.

Returns

The first observation conforming to: observation_space.
dict: The episode-level information.: Note that this is not part of env_info provided in step(). It contains information of he entire episode， which could be needed to determine the first action (e.g. in the case of goal-conditioned or MTRL.)

Return type

numpy.ndarray

abstract step(action)¶

Steps the environment with the action and returns a EnvStep.

If the environment returned the last EnvStep of a sequence (either of type TERMINAL or TIMEOUT) at the previous step, this call to step() will start a new sequence and action will be ignored.

If spec.max_episode_length is reached after applying the action and the environment has not terminated the episode, step() should return a EnvStep with step_type==StepType.TIMEOUT.

If possible, update the visualization display as well.

Parameters: action (object) – A NumPy array, or a nested dict, list or tuple of arrays conforming to action_space.
Returns: The environment step resulting from the action.
Return type: EnvStep
Raises: RuntimeError – if step() is called after the environment has been constructed and reset() has not been called.

abstract render(mode)¶

Renders the environment.

The set of supported modes varies per environment. By convention, if mode is:

rgb_array: Return an numpy.ndarray with shape (x, y, 3) and type
uint8, representing RGB values for an x-by-y pixel image, suitable for turning into a video.
ansi: Return a string (str) or StringIO.StringIO containing a
terminal-style text representation. The text can include newlines and ANSI escape sequences (e.g. for colors).

Make sure that your class’s render_modes includes the list of supported modes.

For example:

class MyEnv(Environment):
    def render_modes(self):
        return ['rgb_array', 'ansi']

    def render(self, mode):
        if mode == 'rgb_array':
            return np.array(...)  # return RGB frame for video
        elif mode == 'ansi':
            ...  # return text output
        else:
            raise ValueError('Supported render modes are {}, but '
                             'got render mode {} instead.'.format(
                                 self.render_modes, mode))

Parameters: mode (str) – the mode to render with. The string must be present in self.render_modes.

abstract visualize()¶

Creates a visualization of the environment.

This function should be called only once after reset() to set up the visualization display. The visualization should be updated when the environment is changed (i.e. when step() is called.)

Calling close() will deallocate any resources and close any windows created by visualize(). If close() is not explicitly called, the visualization will be closed when the environment is destructed (i.e. garbage collected).

abstract close()¶

Closes the environment.

This method should close all windows invoked by visualize().

Override this function in your subclass to perform any necessary cleanup.

Environments will automatically close() themselves when they are garbage collected or when the program exits.

class EnvSpec(observation_space, action_space, max_episode_length=None)¶

Bases: InOutSpec

Describes the observations, actions, and time horizon of an MDP.

Parameters

observation_space (akro.Space) – The observation space of the env.
action_space (akro.Space) – The action space of the env.
max_episode_length (int) – The maximum number of steps allowed in an episode.

property action_space¶

Get action space.

Returns: Action space of the env.
Return type: akro.Space

property observation_space¶

Get observation space of the env.

Returns: Observation space.
Return type: akro.Space

max_episode_length :int or None¶

input_space :akro.Space¶

output_space :akro.Space¶

class EnvStep¶

A tuple representing a single step returned by the environment.

env_spec¶

Specification for the environment from which this data was sampled.

Type: EnvSpec

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

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 containing environment state information.

Type: dict

step_type¶

a StepType enum value. Can either be StepType.FIRST, StepType.MID, StepType.TERMINAL, StepType.TIMEOUT.

Type: StepType

property first¶

Whether this TimeStep is the first of a sequence.

Type: bool

property mid¶

Whether this TimeStep is in the mid of a sequence.

Type: bool

property terminal¶

Whether this TimeStep records a termination condition.

Type: bool

property timeout¶

Whether this TimeStep records a time out condition.

Type: bool

property last¶

Whether this TimeStep is the last of a sequence.

Type: bool

env_spec :EnvSpec¶

action :numpy.ndarray¶

reward :float¶

observation :numpy.ndarray¶

env_info :Dict[str, np.ndarray or dict]¶

step_type :garage._dtypes.StepType¶

class InOutSpec¶

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

input_space :akro.Space¶

output_space :akro.Space¶

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(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()¶: The name of the Enum member.

value()¶: The value of the Enum member.

class Wrapper(env)¶

Bases: Environment

A wrapper for an environment that implements the Environment API.

property action_space¶

The action space specification.

Type: akro.Space

property observation_space¶

The observation space specification.

Type: akro.Space

property spec¶

The environment specification.

Type: EnvSpec

property render_modes¶

A list of string representing the supported render modes.

Type: list

property unwrapped¶

The inner environment.

Type: garage.Environment

step(action)¶

Step the wrapped env.

Parameters: action (np.ndarray) – An action provided by the agent.
Returns: The environment step resulting from the action.
Return type: EnvStep

reset()¶

Reset the wrapped env.

Returns

The first observation conforming to: observation_space.
dict: The episode-level information.: Note that this is not part of env_info provided in step(). It contains information of he entire episode， which could be needed to determine the first action (e.g. in the case of goal-conditioned or MTRL.)

Return type

numpy.ndarray

render(mode)¶

Render the wrapped environment.

Parameters: mode (str) – the mode to render with. The string must be present in self.render_modes.
Returns: the return value for render, depending on each env.
Return type: object

visualize()¶: Creates a visualization of the wrapped environment.

close()¶: Close the wrapped env.

wrap_experiment(function=None, *, log_dir=None, prefix='experiment', name=None, snapshot_mode='last', snapshot_gap=1, archive_launch_repo=True, name_parameters=None, use_existing_dir=False, x_axis='TotalEnvSteps')¶

Decorate a function to turn it into an ExperimentTemplate.

When invoked, the wrapped function will receive an ExperimentContext, which will contain the log directory into which the experiment should log information.

This decorator can be invoked in two differed ways.

Without arguments, like this:

@wrap_experiment def my_experiment(ctxt, seed, lr=0.5):

…

Or with arguments:

@wrap_experiment(snapshot_mode=’all’) def my_experiment(ctxt, seed, lr=0.5):

…

All arguments must be keyword arguments.

Parameters

function (callable or None) – The experiment function to wrap.
log_dir (str or None) – The full log directory to log to. Will be computed from name if omitted.
name (str or None) – The name of this experiment template. Will be filled from the wrapped function’s name if omitted.
prefix (str) – Directory under data/local in which to place the experiment directory.
snapshot_mode (str) – Policy for which snapshots to keep (or make at all). Can be either “all” (all iterations will be saved), “last” (only the last iteration will be saved), “gap” (every snapshot_gap iterations are saved), or “none” (do not save snapshots).
snapshot_gap (int) – Gap between snapshot iterations. Waits this number of iterations before taking another snapshot.
archive_launch_repo (bool) – Whether to save an archive of the repository containing the launcher script. This is a potentially expensive operation which is useful for ensuring reproducibility.
name_parameters (str or None) – Parameters to insert into the experiment name. Should be either None (the default), ‘all’ (all parameters will be used), or ‘passed’ (only passed parameters will be used). The used parameters will be inserted in the order they appear in the function definition.
use_existing_dir (bool) – If true, (re)use the directory for this experiment, even if it already contains data.
x_axis (str) – Key to use for x axis of plots.

Returns

The wrapped function.

Return type

callable

class TFTrainer(snapshot_config, sess=None)¶

Bases: Trainer

This class implements a trainer for TensorFlow algorithms.

A trainer provides a default TensorFlow session using python context. This is useful for those experiment components (e.g. policy) that require a TensorFlow session during construction.

Use trainer.setup(algo, env) to setup algorithm and environment for trainer and trainer.train() to start training.

Parameters

snapshot_config (garage.experiment.SnapshotConfig) – The snapshot configuration used by Trainer to create the snapshotter. If None, it will create one with default settings.
sess (tf.Session) – An optional TensorFlow session. A new session will be created immediately if not provided.

Note

When resume via command line, new snapshots will be saved into the SAME directory if not specified.

When resume programmatically, snapshot directory should be specify manually or through @wrap_experiment interface.

Examples

# to train with TFTrainer() as trainer:

env = gym.make(‘CartPole-v1’) policy = CategoricalMLPPolicy(

env_spec=env.spec, hidden_sizes=(32, 32))

algo = TRPO(
env=env, policy=policy, baseline=baseline, max_episode_length=100, discount=0.99, max_kl_step=0.01)

trainer.setup(algo, env) trainer.train(n_epochs=100, batch_size=4000)

# to resume immediately. with TFTrainer() as trainer:

trainer.restore(resume_from_dir) trainer.resume()

# to resume with modified training arguments. with TFTrainer() as trainer:

trainer.restore(resume_from_dir) trainer.resume(n_epochs=20)

property total_env_steps¶

Total environment steps collected.

Returns: Total environment steps collected.
Return type: int

setup(algo, env)¶

Set up trainer and sessions for algorithm and environment.

This method saves algo and env within trainer and creates a sampler, and initializes all uninitialized variables in session.

Note

After setup() is called all variables in session should have been initialized. setup() respects existing values in session so policy weights can be loaded before setup().

Parameters

algo (RLAlgorithm) – An algorithm instance.
env (Environment) – An environment instance.

initialize_tf_vars()¶: Initialize all uninitialized variables in session.

obtain_episodes(itr, batch_size=None, agent_update=None, env_update=None)¶

Obtain one batch of episodes.

Parameters

itr (int) – Index of iteration (epoch).
batch_size (int) – Number of steps in batch. This is a hint that the sampler may or may not respect.
agent_update (object) – Value which will be passed into the agent_update_fn before doing sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.
env_update (object) – Value which will be passed into the env_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.

Raises

ValueError – If the trainer was initialized without a sampler, or batch_size wasn’t provided here or to train.

Returns

Batch of episodes.

Return type

EpisodeBatch

obtain_samples(itr, batch_size=None, agent_update=None, env_update=None)¶

Obtain one batch of samples.

Parameters

itr (int) – Index of iteration (epoch).
batch_size (int) – Number of steps in batch. This is a hint that the sampler may or may not respect.
agent_update (object) – Value which will be passed into the agent_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.
env_update (object) – Value which will be passed into the env_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.

Raises

ValueError – Raised if the trainer was initialized without a sampler, or batch_size wasn’t provided here or to train.

Returns

One batch of samples.

Return type

list[dict]

save(epoch)¶

Save snapshot of current batch.

Parameters: epoch (int) – Epoch.
Raises: NotSetupError – if save() is called before the trainer is set up.

restore(from_dir, from_epoch='last')¶

Restore experiment from snapshot.

Parameters

from_dir (str) – Directory of the pickle file to resume experiment from.
from_epoch (str or int) – The epoch to restore from. Can be ‘first’, ‘last’ or a number. Not applicable when snapshot_mode=’last’.

Returns

Arguments for train().

Return type

TrainArgs

log_diagnostics(pause_for_plot=False)¶

Log diagnostics.

Parameters: pause_for_plot (bool) – Pause for plot.

train(n_epochs, batch_size=None, plot=False, store_episodes=False, pause_for_plot=False)¶

Start training.

Parameters

n_epochs (int) – Number of epochs.
batch_size (int or None) – Number of environment steps in one batch.
plot (bool) – Visualize an episode from the policy after each epoch.
store_episodes (bool) – Save episodes in snapshot.
pause_for_plot (bool) – Pause for plot.

Raises

NotSetupError – If train() is called before setup().

Returns

The average return in last epoch cycle.

Return type

float

step_epochs()¶

Step through each epoch.

This function returns a magic generator. When iterated through, this generator automatically performs services such as snapshotting and log management. It is used inside train() in each algorithm.

The generator initializes two variables: self.step_itr and self.step_episode. To use the generator, these two have to be updated manually in each epoch, as the example shows below.

Yields: int – The next training epoch.

Examples

for epoch in trainer.step_epochs():: trainer.step_episode = trainer.obtain_samples(…) self.train_once(…) trainer.step_itr += 1

resume(n_epochs=None, batch_size=None, plot=None, store_episodes=None, pause_for_plot=None)¶

Resume from restored experiment.

This method provides the same interface as train().

If not specified, an argument will default to the saved arguments from the last call to train().

Parameters

n_epochs (int) – Number of epochs.
batch_size (int) – Number of environment steps in one batch.
plot (bool) – Visualize an episode from the policy after each epoch.
store_episodes (bool) – Save episodes in snapshot.
pause_for_plot (bool) – Pause for plot.

Raises

NotSetupError – If resume() is called before restore().

Returns

The average return in last epoch cycle.

Return type

float

get_env_copy()¶

Get a copy of the environment.

Returns: An environment instance.
Return type: Environment

class Trainer(snapshot_config)¶

Base class of trainer.

Use trainer.setup(algo, env) to setup algorithm and environment for trainer and trainer.train() to start training.

Parameters: snapshot_config (garage.experiment.SnapshotConfig) – The snapshot configuration used by Trainer to create the snapshotter. If None, it will create one with default settings.

Note

For the use of any TensorFlow environments, policies and algorithms, please use TFTrainer().

Examples

# to train
trainer = Trainer()
env = Env(…)
policy = Policy(…)
algo = Algo(
env=env,
policy=policy,
…)
trainer.setup(algo, env)
trainer.train(n_epochs=100, batch_size=4000)

# to resume immediately.
trainer = Trainer()
trainer.restore(resume_from_dir)
trainer.resume()

# to resume with modified training arguments.
trainer = Trainer()
trainer.restore(resume_from_dir)
trainer.resume(n_epochs=20)

property total_env_steps¶

Total environment steps collected.

Returns: Total environment steps collected.
Return type: int

setup(algo, env)¶

Set up trainer for algorithm and environment.

This method saves algo and env within trainer and creates a sampler.

Note

After setup() is called all variables in session should have been initialized. setup() respects existing values in session so policy weights can be loaded before setup().

Parameters

algo (RLAlgorithm) – An algorithm instance. If this algo want to use samplers, it should have a _sampler field.
env (Environment) – An environment instance.

obtain_episodes(itr, batch_size=None, agent_update=None, env_update=None)¶

Obtain one batch of episodes.

Parameters

itr (int) – Index of iteration (epoch).
batch_size (int) – Number of steps in batch. This is a hint that the sampler may or may not respect.
agent_update (object) – Value which will be passed into the agent_update_fn before doing sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.
env_update (object) – Value which will be passed into the env_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.

Raises

ValueError – If the trainer was initialized without a sampler, or batch_size wasn’t provided here or to train.

Returns

Batch of episodes.

Return type

EpisodeBatch

obtain_samples(itr, batch_size=None, agent_update=None, env_update=None)¶

Obtain one batch of samples.

Parameters

itr (int) – Index of iteration (epoch).
batch_size (int) – Number of steps in batch. This is a hint that the sampler may or may not respect.
agent_update (object) – Value which will be passed into the agent_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.
env_update (object) – Value which will be passed into the env_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.

Raises

ValueError – Raised if the trainer was initialized without a sampler, or batch_size wasn’t provided here or to train.

Returns

One batch of samples.

Return type

list[dict]

save(epoch)¶

Save snapshot of current batch.

Parameters: epoch (int) – Epoch.
Raises: NotSetupError – if save() is called before the trainer is set up.

restore(from_dir, from_epoch='last')¶

Restore experiment from snapshot.

Parameters

from_dir (str) – Directory of the pickle file to resume experiment from.
from_epoch (str or int) – The epoch to restore from. Can be ‘first’, ‘last’ or a number. Not applicable when snapshot_mode=’last’.

Returns

Arguments for train().

Return type

TrainArgs

log_diagnostics(pause_for_plot=False)¶

Log diagnostics.

Parameters: pause_for_plot (bool) – Pause for plot.

train(n_epochs, batch_size=None, plot=False, store_episodes=False, pause_for_plot=False)¶

Start training.

Parameters

n_epochs (int) – Number of epochs.
batch_size (int or None) – Number of environment steps in one batch.
plot (bool) – Visualize an episode from the policy after each epoch.
store_episodes (bool) – Save episodes in snapshot.
pause_for_plot (bool) – Pause for plot.

Raises

NotSetupError – If train() is called before setup().

Returns

The average return in last epoch cycle.

Return type

float

step_epochs()¶

Step through each epoch.

This function returns a magic generator. When iterated through, this generator automatically performs services such as snapshotting and log management. It is used inside train() in each algorithm.

The generator initializes two variables: self.step_itr and self.step_episode. To use the generator, these two have to be updated manually in each epoch, as the example shows below.

Yields: int – The next training epoch.

Examples

for epoch in trainer.step_epochs():: trainer.step_episode = trainer.obtain_samples(…) self.train_once(…) trainer.step_itr += 1

resume(n_epochs=None, batch_size=None, plot=None, store_episodes=None, pause_for_plot=None)¶

Resume from restored experiment.

This method provides the same interface as train().

If not specified, an argument will default to the saved arguments from the last call to train().

Parameters

n_epochs (int) – Number of epochs.
batch_size (int) – Number of environment steps in one batch.
plot (bool) – Visualize an episode from the policy after each epoch.
store_episodes (bool) – Save episodes in snapshot.
pause_for_plot (bool) – Pause for plot.

Raises

NotSetupError – If resume() is called before restore().

Returns

The average return in last epoch cycle.

Return type

float

get_env_copy()¶

Get a copy of the environment.

Returns: An environment instance.
Return type: Environment

garage¶

`garage`¶