tf_agents.bandits.environments.bernoulli_action_mask_tf_environment.BernoulliActionMaskTFEnvironment

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An environment wrapper that adds action masks to observations.

Inherits From: BanditTFEnvironment, TFEnvironment

tf_agents.bandits.environments.bernoulli_action_mask_tf_environment.BernoulliActionMaskTFEnvironment(
    original_environment: tf_agents.bandits.environments.bandit_tf_environment.BanditTFEnvironment,
    action_constraint_join_fn: Callable[[tf_agents.typing.types.TensorSpec, tf_agents.typing.types.TensorSpec], tf_agents.typing.types.TensorSpec],
    action_probability: float
)

Args
original_environment	Instance of BanditTFEnvironment. This environment will be wrapped.
action_constraint_join_fn	A function that joins the osbervation from the original environment with the generated masks.
action_probability	The probability that any action in the action space is allower by the generated mask.

Attributes
batch_size
batched
name
original_environment

Methods

action_spec

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action_spec()

Describes the specs of the Tensors expected by step(action).

action can be a single Tensor, or a nested dict, list or tuple of Tensors.

Returns
An single TensorSpec, or a nested dict, list or tuple of TensorSpec objects, which describe the shape and dtype of each Tensor expected by step().

current_time_step

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current_time_step()

Returns the current TimeStep.

Returns
A TimeStep namedtuple containing: step_type: A StepType value. reward: Reward at this time_step. discount: A discount in the range [0, 1]. observation: A Tensor, or a nested dict, list or tuple of Tensors corresponding to observation_spec().

observation_spec

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observation_spec()

Defines the TensorSpec of observations provided by the environment.

Returns
A TensorSpec, or a nested dict, list or tuple of TensorSpec objects, which describe the observation.

render

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render()

Renders a frame from the environment.

Raises
NotImplementedError	If the environment does not support rendering.

reset

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reset()

Resets the environment and returns the current time_step.

Returns
A TimeStep namedtuple containing: step_type: A StepType value. reward: Reward at this time_step. discount: A discount in the range [0, 1]. observation: A Tensor, or a nested dict, list or tuple of Tensors corresponding to observation_spec().

reward_spec

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reward_spec()

Defines the TensorSpec of rewards provided by the environment.

Returns
A TensorSpec, or a nested dict, list or tuple of TensorSpec objects, which describe the reward.

step

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step(
    action
)

Steps the environment according to the action.

If the environment returned a TimeStep with StepType.LAST at the previous step, this call to step should reset the environment (note that it is expected that whoever defines this method, calls reset in this case), start a new sequence and action will be ignored.

This method will also 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.

Expected sequences look like:

time_step -> action -> next_time_step

The action should depend on the previous time_step for correctness.

Args
action	A Tensor, or a nested dict, list or tuple of Tensors corresponding to action_spec().

Returns
A TimeStep namedtuple containing: step_type: A StepType value. reward: Reward at this time_step. discount: A discount in the range [0, 1]. observation: A Tensor, or a nested dict, list or tuple of Tensors corresponding to observation_spec().

time_step_spec

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time_step_spec()

Describes the TimeStep specs of Tensors returned by step().

Returns
A TimeStep namedtuple containing TensorSpec objects defining the Tensors returned by step(), i.e. (step_type, reward, discount, observation).