garage.envs.task_name_wrapper

Wrapper for adding an environment info to track task ID.

class TaskNameWrapper(env, *, task_name=None, task_id=None)

Bases: garage.Wrapper

Add task_name or task_id to env infos.

Parameters

• env (gym.Env) – The environment to wrap.

• task_name (str or None) – Task name to be added, if any.

• task_id (int or None) – Task ID to be added, if any.

step(self, action)

gym.Env step for the active task env.

Parameters

action (np.ndarray) – Action performed by the agent in the environment.

Returns

np.ndarray: Agent’s observation of the current environment. float: Amount of reward yielded by previous action. bool: True iff the episode has ended. dict[str, np.ndarray]: Contains auxiliary diagnostic

information about this time-step.

Return type

tuple

property action_space(self)

akro.Space: The action space specification.

property observation_space(self)

akro.Space: The observation space specification.

property spec(self)

EnvSpec: The environment specification.

property render_modes(self)

list: A list of string representing the supported render modes.

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.

property unwrapped(self)

garage.Environment: The inner environment.