`garage._environment`¶

Base Garage Environment API.

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

Bases: garage.InOutSpec

Inheritance diagram of garage._environment.EnvSpec

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()

Inheritance diagram of garage._environment.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

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 Environment¶

Bases: abc.ABC

Inheritance diagram of garage._environment.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.

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 Wrapper(env)¶

Bases: garage._environment.Environment

Inheritance diagram of garage._environment.Wrapper

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.

garage._environment¶

`garage._environment`¶