# garage.experiment.task_sampler¶

Efficient and general interfaces for sampling tasks for Meta-RL.

class TaskSampler

Bases: abc.ABC

Class for sampling batches of tasks, represented as ~EnvUpdates.

n_tasks

Number of tasks, if known and finite.

Type

int or None

abstract sample(self, n_tasks, with_replacement=False)

Sample a list of environment updates.

Parameters

• with_replacement (bool) – Whether tasks can repeat when sampled. Note that if more tasks are sampled than exist, then tasks may repeat, but only after every environment has been included at least once in this batch. Ignored for continuous task spaces.

Returns

Batch of sampled environment updates, which, when

invoked on environments, will configure them with new tasks. See EnvUpdate for more information.

Return type
property n_tasks(self)

int or None: The number of tasks if known and finite.

class ConstructEnvsSampler(env_constructors)

Generally, this is used when the different tasks are completely different environments.

Parameters

env_constructors (list[Callable[Environment]]) – Callables that produce environments (for example, environment types).

property n_tasks(self)

sample(self, n_tasks, with_replacement=False)

Sample a list of environment updates.

Parameters

• with_replacement (bool) – Whether tasks can repeat when sampled. Note that if more tasks are sampled than exist, then tasks may repeat, but only after every environment has been included at least once in this batch. Ignored for continuous task spaces.

Returns

Batch of sampled environment updates, which, when

invoked on environments, will configure them with new tasks. See EnvUpdate for more information.

Return type
class SetTaskSampler(env_constructor, *, env=None, wrapper=None)

This is used for environments that implement sample_tasks and set_task. For example, HalfCheetahVelEnv, as implemented in Garage.

Parameters
• env_constructor (type) – Type of the environment.

• env (garage.Environment or None) – Instance of env_constructor to sample from (will be constructed if not provided).

• wrapper (Callable[garage.Environment, garage.Environment] or None) – Wrapper function to apply to environment.

property n_tasks(self)

int or None: The number of tasks if known and finite.

sample(self, n_tasks, with_replacement=False)

Sample a list of environment updates.

Parameters

• with_replacement (bool) – Whether tasks can repeat when sampled. Note that if more tasks are sampled than exist, then tasks may repeat, but only after every environment has been included at least once in this batch. Ignored for continuous task spaces.

Returns

Batch of sampled environment updates, which, when

invoked on environments, will configure them with new tasks. See EnvUpdate for more information.

Return type
class EnvPoolSampler(envs)

TaskSampler that samples from a finite pool of environments.

This can be used with any environments, but is generally best when using in-process samplers with environments that are expensive to construct.

Parameters

envs (list[Environment]) – List of environments to use as a pool.

property n_tasks(self)

sample(self, n_tasks, with_replacement=False)

Sample a list of environment updates.

Parameters

• with_replacement (bool) – Whether tasks can repeat when sampled. Since this cannot be easily implemented for an object pool, setting this to True results in ValueError.

Raises

ValueError – If the number of requested tasks is larger than the pool, or with_replacement is set.

Returns

Batch of sampled environment updates, which, when

invoked on environments, will configure them with new tasks. See EnvUpdate for more information.

Return type
grow_pool(self, new_size)

Increase the size of the pool by copying random tasks in it.

Note that this only copies the tasks already in the pool, and cannot create new original tasks in any way.

Parameters

new_size (int) – Size the pool should be after growning.

MW_TASKS_PER_ENV = 50
class MetaWorldTaskSampler(benchmark, kind, wrapper=None, add_env_onehot=False)

TaskSampler that distributes a Meta-World benchmark across workers.

Parameters
• benchmark (metaworld.Benchmark) – Benchmark to sample tasks from.

• kind (str) – Must be either ‘test’ or ‘train’. Determines whether to sample training or test tasks from the Benchmark.

• 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’ or ‘test’. Also raisd if add_env_onehot is used on a metaworld meta learning (not multi-task) benchmark.

property n_tasks(self)

sample(self, n_tasks, with_replacement=False)

Sample a list of environment updates.

Note that this will always return environments in the same order, to make parallel sampling across workers efficient. If randomizing the environment order is required, shuffle the result of this method.

Parameters
• n_tasks (int) – Number of updates to sample. Must be a multiple of the number of env classes in the benchmark (e.g. 1 for MT/ML1, 10 for MT10, 50 for MT50). Tasks for each environment will be grouped to be adjacent to each other.

• with_replacement (bool) – Whether tasks can repeat when sampled. Since this cannot be easily implemented for an object pool, setting this to True results in ValueError.

Raises

ValueError – If the number of requested tasks is not equal to the number of classes or the number of total tasks.

Returns

Batch of sampled environment updates, which, when

invoked on environments, will configure them with new tasks. See EnvUpdate for more information.

Return type