tf_agents.environments.FlattenObservationsWrapper

Wraps an environment and flattens nested multi-dimensional observations.

Inherits From: PyEnvironmentBaseWrapper, PyEnvironment

tf_agents.environments.FlattenObservationsWrapper(
    env: tf_agents.environments.PyEnvironment,
    observations_allowlist: Optional[Sequence[Text]] = None,
    handle_auto_reset: bool = False
)

The observation returned by the environment is a multi-dimensional sequence of items of varying lengths.

timestep.observation_spec = {'position': ArraySpec(shape=(4,), dtype=float32), 'target': ArraySpec(shape=(5,), dtype=float32)}

timestep.observation = {'position': [1,2,3,4], target': [5,6,7,8,9]}

By packing the observation, we reduce the dimensions into a single dimension and concatenate the values of all the observations into one array.

timestep.observation_spec = ( 'packed_observations': ArraySpec(shape=(9,), dtype=float32)

timestep.observation = [1,2,3,4,5,6,7,8,9] # Array of len-9.

env A py_environment.PyEnvironment environment to wrap.
observations_allowlist A list of observation keys that want to be observed from the environment. All other observations returned are filtered out. If not provided, all observations will be kept. Additionally, if this is provided, the environment is expected to return a dictionary of observations.
handle_auto_reset When True the base class will handle auto_reset of the Environment.

ValueError If the current environment does not return a dictionary of observations and observations_allowlist is provided.
ValueError If the observation_allowlist keys are not found in the environment.

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()).

Methods

action_spec

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

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

Frees any resources used by the environment.

Implement this method for an environment backed by an external process.

This method be used directly

env = Env(...)
# Use env.
env.close()

or via a context manager

with Env(...) as env:
  # Use env.

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() -> Any

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

View source

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

Defines the observations provided by the environment.

Returns
An ArraySpec with a shape of the total length of observations kept.

render

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

Renders the environment.

Args
mode One of ['rgb_array', 'human']. Renders to an numpy array, or brings up a window where the environment can be visualized.

Returns
An ndarray of shape [width, height, 3] denoting an RGB image if mode is rgb_array. Otherwise return nothing and render directly to a display window.

Raises
NotImplementedError If the environment does not support rendering.

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(
    seed: tf_agents.typing.types.Seed
) -> tf_agents.typing.types.Seed

Seeds the environment.

Args
seed Value to use as seed for the environment.

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.

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.

wrapped_env

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wrapped_env() -> Any

__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.