tf_agents.environments.RandomTFEnvironment

Randomly generates observations following the given observation_spec.

Inherits From: TFEnvironment

tf_agents.environments.RandomTFEnvironment(
    time_step_spec, action_spec, batch_size=1, episode_end_probability=0.1
)

If an action_spec is provided, it validates that the actions used to step the environment are compatible with the given spec.

time_step_spec A TimeStep namedtuple containing TensorSpecs defining the Tensors returned by step() (step_type, reward, discount, and observation).
action_spec A nest of BoundedTensorSpec representing the actions of the environment.
batch_size The batch size expected for the actions and observations.
episode_end_probability Probability an episode will end when the environment is stepped.

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().

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).