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tf_agents.bandits.environments.random_bandit_environment.RandomBanditEnvironment

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Bandit environment that returns random observations and rewards.

Inherits From: BanditTFEnvironment

tf_agents.bandits.environments.random_bandit_environment.RandomBanditEnvironment(
    observation_distribution, reward_distribution, action_spec=None
)

Args:

  • observation_distribution: a tensorflow_probability.Distribution. Batches of observations will be drawn from this distribution. The batch_shape of this distribution must have length 1 and be the same as the batch_shape of reward_distribution.
  • reward_distribution: a tensorflow_probability.Distribution. Batches of rewards will be drawn from this distribution. The batch_shape of this distribution must have length 1 and be the same as the batch_shape of observation_distribution.
  • action_spec: a TensorSpec describing the expected action. Note that actions are ignored and do not affect rewards.

Attributes:

  • batch_size
  • batched

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().

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).