# garage.envs.metaworld_set_task_env¶

Environment that wraps a MetaWorld benchmark in the set_task interface.

class MetaWorldSetTaskEnv(benchmark=None, kind=None, wrapper=None, add_env_onehot=False)

Environment form of a MetaWorld benchmark.

This class is generally less efficient than using a TaskSampler, if that can be used instead, since each instance of this class internally caches a copy of each environment in the benchmark.

In order to sample tasks from this environment, a benchmark must be passed at construction time.

Parameters
• benchmark (metaworld.Benchmark or None) – The benchmark to wrap.

• kind (str or None) – Whether to use test or train tasks.

• wrapper (Callable[garage.Env, garage.Env] or None) – Wrapper to apply to env instances.

• add_env_onehot (bool) – If true, a one-hot representing the current environment name will be added to the environments. Should only be used with multi-task benchmarks.

Raises

ValueError – If kind is not ‘train’, ‘test’, or None. Also raisd if add_env_onehot is used on a metaworld meta learning (not multi-task) benchmark.

property num_tasks(self)

Part of the set_task environment protocol.

sample_tasks(self, n_tasks)

Part of the set_task environment protocol. To call this method, a benchmark must have been passed in at environment construction.

Parameters

Returns

Return type
set_task(self, task)

Part of the set_task environment protocol.

Parameters

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 envionrment specification.

property render_modes(self)

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

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.