garage

Garage Base.

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 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 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 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.

class Environment

Bases: abc.ABC

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.

action_space

The action space specification.

Type:akro.Space

observation_space

The observation space specification.

Type:akro.Space

spec

The environment specification.

Type:EnvSpec

render_modes

A list of string representing the supported render modes.

See render() for a list of modes.

Type:list

reset(self)

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

step(self, 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.

render(self, 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.

visualize(self)

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).

close(self)

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: garage.InOutSpec

Describes the action and observation spaces of an environment.

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.

action_space

Get action space.

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

observation_space

Get observation space of the env.

Returns:Observation space. Return type:akro.Space

max_episode_length

Get max episode steps.

Returns:The maximum number of steps that an episode Return type:int

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 EnvStep

Bases: collections.namedtuple()

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

first

Whether this TimeStep is the first of a sequence.

Type:bool

mid

Whether this TimeStep is in the mid of a sequence.

Type:bool

terminal

Whether this TimeStep records a termination condition.

Type:bool

timeout

Whether this TimeStep records a time out condition.

Type:bool

last

Whether this TimeStep is the last of a sequence.

Type:bool

count()

Return number of occurrences of value.

index()

Return first index of value.

Raises ValueError if the value is not present.

class Wrapper(env)

Bases: garage._environment.Environment

A wrapper for an environment that implements the Environment API.

action_space

The action space specification.

Type:akro.Space

observation_space

The observation space specification.

Type:akro.Space

spec

The envionrment specification.

Type:EnvSpec

render_modes

A list of string representing the supported render modes.

Type:list

step(self, 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(self)

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(self, 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(self)

Creates a visualization of the wrapped environment.

close(self)

Close the wrapped env.

log_multitask_performance(itr, batch, discount, name_map=None)

Log performance of episodes from multiple tasks.

Parameters:

• itr (int) – Iteration number to be logged.
• batch (EpisodeBatch) – Batch of episodes. The episodes should have either the “task_name” or “task_id” env_infos. If the “task_name” is not present, then name_map is required, and should map from task id’s to task names.
• discount (float) – Discount used in computing returns.
• name_map (dict[int, str] or None) – Mapping from task id’s to task names. Optional if the “task_name” environment info is present. Note that if provided, all tasks listed in this map will be logged, even if there are no episodes present for them.

Returns:

Undiscounted returns averaged across all tasks. Has

shape \((N \bullet [T])\).

Return type:

numpy.ndarray

log_performance(itr, batch, discount, prefix='Evaluation')

Evaluate the performance of an algorithm on a batch of episodes.

Parameters:

• itr (int) – Iteration number.
• batch (EpisodeBatch) – The episodes to evaluate with.
• discount (float) – Discount value, from algorithm’s property.
• prefix (str) – Prefix to add to all logged keys.

Returns:

Undiscounted returns.

Return type:

numpy.ndarray

make_optimizer(optimizer_type, module=None, **kwargs)

Create an optimizer for pyTorch & tensorflow algos.

Parameters:

• optimizer_type (Union[type, tuple[type, dict]]) – Type of optimizer. This can be an optimizer type such as ‘torch.optim.Adam’ or a tuple of type and dictionary, where dictionary contains arguments to initialize the optimizer e.g. (torch.optim.Adam, {‘lr’ : 1e-3})
• module (optional) – If the optimizer type is a torch.optimizer. The torch.nn.Module module whose parameters needs to be optimized must be specify.
• kwargs (dict) – Other keyword arguments to initialize optimizer. This is not used when optimizer_type is tuple.

Returns:

Constructed optimizer.

Return type:

torch.optim.Optimizer

Raises:

ValueError – Raises value error when optimizer_type is tuple, and non-default argument is passed in kwargs.

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)

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.

Returns:

The wrapped function.

Return type:

callable

• garage.envs
• garage.experiment
• garage.misc
• garage.np
• garage.plotter
• garage.replay_buffer
• garage.sampler
• garage.tf
• garage.torch