tf_agents.environments.ParallelPyEnvironment

Batch together environments and simulate them in external processes.

Inherits From: PyEnvironment

tf_agents.environments.ParallelPyEnvironment(
    env_constructors: Sequence[tf_agents.environments.parallel_py_environment.EnvConstructor],
    start_serially: bool = True,
    blocking: bool = False,
    flatten: bool = False
)

The environments are created in external processes by calling the provided callables. This can be an environment class, or a function creating the environment and potentially wrapping it. The returned environment should not access global variables.

env_constructors List of callables that create environments.
start_serially Whether to start environments serially or in parallel.
blocking Whether to step environments one after another.
flatten Boolean, whether to use flatten action and time_steps during communication to reduce overhead.

ValueError If the action or observation specs don't match.

batch_size The batch size of the environment.
batched Whether the environment is batched or not.

If the environment supports batched observations and actions, then overwrite this property to True.

A batched environment takes in a batched set of actions and returns a batched set of observations. This means for all numpy arrays in the input and output nested structures, the first dimension is the batch size.

When batched, the left-most dimension is not part of the action_spec or the observation_spec and corresponds to the batch dimension.

When batched and handle_auto_reset, it checks np.all(steps.is_last()).

envs

Methods

action_spec

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action_spec() -> tf_agents.typing.types.NestedArraySpec

Defines the actions that should be provided to step().

May use a subclass of ArraySpec that specifies additional properties such as min and max bounds on the values.

Returns
An ArraySpec, or a nested dict, list or tuple of ArraySpecs.

close

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close() -> None

Close all external process.

current_time_step

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current_time_step() -> tf_agents.trajectories.TimeStep

Returns the current timestep.

discount_spec

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discount_spec() -> tf_agents.typing.types.NestedArraySpec

Defines the discount that are returned by step().

Override this method to define an environment that uses non-standard discount values, for example an environment with array-valued discounts.

Returns
An ArraySpec, or a nested dict, list or tuple of ArraySpecs.

get_info

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get_info() -> tf_agents.typing.types.NestedArray

Returns the environment info returned on the last step.

Returns
Info returned by last call to step(). None by default.

Raises
NotImplementedError If the environment does not use info.

get_state

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get_state() -> Any

Returns the state of the environment.

The state contains everything required to restore the environment to the current configuration. This can contain e.g.

  • The current time_step.
  • The number of steps taken in the environment (for finite horizon MDPs).
  • Hidden state (for POMDPs).

Callers should not assume anything about the contents or format of the returned state. It should be treated as a token that can be passed back to set_state() later.

Note that the returned state handle should not be modified by the environment later on, and ensuring this (e.g. using copy.deepcopy) is the responsibility of the environment.

Returns
state The current state of the environment.

observation_spec

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observation_spec() -> tf_agents.typing.types.NestedArraySpec

Defines the observations provided by the environment.

May use a subclass of ArraySpec that specifies additional properties such as min and max bounds on the values.

Returns
An ArraySpec, or a nested dict, list or tuple of ArraySpecs.

render

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render(
    mode: Text = 'rgb_array'
) -> tf_agents.typing.types.NestedArray

Renders the environment.

Args
mode Rendering mode. Currently only 'rgb_array' is supported because this is a batched environment.

Returns
An ndarray of shape [batch_size, width, height, 3] denoting RGB images (for mode=rgb_array).

Raises
NotImplementedError If the environment does not support rendering, or any other mode than rgb_array is given.

reset

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reset() -> tf_agents.trajectories.TimeStep

Starts a new sequence and returns the first TimeStep of this sequence.

Returns
A TimeStep namedtuple containing: step_type: A StepType of FIRST. reward: 0.0, indicating the reward. discount: 1.0, indicating the discount. observation: A NumPy array, or a nested dict, list or tuple of arrays corresponding to observation_spec().

reward_spec

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reward_spec() -> tf_agents.typing.types.NestedArraySpec

Defines the rewards that are returned by step().

Override this method to define an environment that uses non-standard reward values, for example an environment with array-valued rewards.

Returns
An ArraySpec, or a nested dict, list or tuple of ArraySpecs.

seed

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seed(
    seeds: Sequence[tf_agents.typing.types.Seed]
) -> Sequence[Any]

Seeds the parallel environments.

set_state

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set_state(
    state: Any
) -> None

Restores the environment to a given state.

See definition of state in the documentation for get_state().

Args
state A state to restore the environment to.

should_reset

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should_reset(
    current_time_step: tf_agents.trajectories.TimeStep
) -> bool

Whether the Environmet should reset given the current timestep.

By default it only resets when all time_steps are LAST.

Args
current_time_step The current TimeStep.

Returns
A bool indicating whether the Environment should reset or not.

start

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start() -> None

step

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step(
    action: tf_agents.typing.types.NestedArray
) -> tf_agents.trajectories.TimeStep

Updates the environment according to the action and returns a TimeStep.

If the environment returned a TimeStep with StepType.LAST at the previous step the implementation of _step in the environment should call reset to start a new sequence and ignore action.

This method will start a new sequence if called after the environment has been constructed and reset has not been called. In this case action will be ignored.

If should_reset(current_time_step) is True, then this method will reset by itself. In this case action will be ignored.

Args
action A NumPy array, or a nested dict, list or tuple of arrays corresponding to action_spec().

Returns
A TimeStep namedtuple containing: step_type: A StepType value. reward: A NumPy array, reward value for this timestep. discount: A NumPy array, discount in the range [0, 1]. observation: A NumPy array, or a nested dict, list or tuple of arrays corresponding to observation_spec().

time_step_spec

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time_step_spec() -> tf_agents.trajectories.TimeStep

Describes the TimeStep fields returned by step().

Override this method to define an environment that uses non-standard values for any of the items returned by step(). For example, an environment with array-valued rewards.

Returns
A TimeStep namedtuple containing (possibly nested) ArraySpecs defining the step_type, reward, discount, and observation structure.

__enter__

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__enter__()

Allows the environment to be used in a with-statement context.

__exit__

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__exit__(
    unused_exception_type, unused_exc_value, unused_traceback
)

Allows the environment to be used in a with-statement context.