garage.envs.task_onehot_wrapper

Wrapper for appending one-hot task encodings to individual task envs.

See ~TaskOnehotWrapper.wrap_env_list for the main way of using this module.

class TaskOnehotWrapper(env, task_index, n_total_tasks)

Bases: garage.Wrapper

Append a one-hot task representation to an environment.

See TaskOnehotWrapper.wrap_env_list for the recommended way of creating this class.

Parameters:

• env (Environment) – The environment to wrap.
• task_index (int) – The index of this task among the tasks.
• n_total_tasks (int) – The number of total tasks.

observation_space

The observation space specification.

Type:akro.Space

spec

Return the environment specification.

Returns:The envionrment specification. Return type:EnvSpec

action_space

The action space specification.

Type:akro.Space

render_modes

A list of string representing the supported render modes.

Type:list

reset(self)

Sample new task and call reset on new task 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

step(self, action)

Environment step for the active task env.

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

classmethod wrap_env_list(cls, envs)

Wrap a list of environments, giving each environment a one-hot.

This is the primary way of constructing instances of this class. It’s mostly useful when training multi-task algorithms using a multi-task aware sampler.

For example: ‘’’ .. code-block:: python

envs = get_mt10_envs() wrapped = TaskOnehotWrapper.wrap_env_list(envs) sampler = runner.make_sampler(LocalSampler, env=wrapped)

‘’‘

Parameters:

• envs (list[Environment]) – List of environments to wrap. Note
• the (that) – order these environments are passed in determines the value of their one-hot encoding. It is essential that this list is always in the same order, or the resulting encodings will be inconsistent.

Returns:

The wrapped environments.

Return type:

list[TaskOnehotWrapper]

classmethod wrap_env_cons_list(cls, env_cons)

Wrap a list of environment constructors, giving each a one-hot.

This function is useful if you want to avoid constructing any environments in the main experiment process, and are using a multi-task aware remote sampler (i.e. ~RaySampler).

For example: ‘’’ .. code-block:: python

env_constructors = get_mt10_env_cons() wrapped = TaskOnehotWrapper.wrap_env_cons_list(env_constructors) env_updates = [NewEnvUpdate(wrapped_con)

for wrapped_con in wrapped]

sampler = runner.make_sampler(RaySampler, env=env_updates)

‘’‘

Parameters:

• env_cons (list[Callable[Environment]]) – List of environment
• constructor – to wrap. Note that the order these constructors are passed in determines the value of their one-hot encoding. It is essential that this list is always in the same order, or the resulting encodings will be inconsistent.

Returns:

The wrapped environments.

Return type:

list[Callable[TaskOnehotWrapper]]

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.