tf_agents.trajectories.trajectory.Trajectory

A tuple that represents a trajectory.

tf_agents.trajectories.trajectory.Trajectory(
    step_type, observation, action, policy_info, next_step_type, reward, discount
)

Used in the notebooks

Used in the tutorials

A Trajectory represents a sequence of aligned time steps. It captures the observation, step_type from current time step with the computed action and policy_info. Discount, reward and next_step_type come from the next time step.

step_type A StepType.
observation An array (tensor), or a nested dict, list or tuple of arrays (tensors) that represents the observation.
action An array/a tensor, or a nested dict, list or tuple of actions. This represents action generated according to the observation.
policy_info An arbitrary nest that contains auxiliary information related to the action. Note that this does not include the policy/RNN state which was used to generate the action.
next_step_type The StepType of the next time step.
reward An array/a tensor, or a nested dict, list, or tuple of rewards. This represents the rewards and/or constraint satisfiability after performing the action in an environment.
discount A scalar that representing the discount factor to multiply with future rewards.

Methods

is_boundary

View source

is_boundary() -> tf_agents.typing.types.Bool

is_first

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is_first() -> tf_agents.typing.types.Bool

is_last

View source

is_last() -> tf_agents.typing.types.Bool

is_mid

View source

is_mid() -> tf_agents.typing.types.Bool

replace

View source

replace(
    **kwargs
) -> "Trajectory"

Exposes as namedtuple._replace.

Usage:

  new_trajectory = trajectory.replace(policy_info=())

This returns a new trajectory with an empty policy_info.

Args
**kwargs key/value pairs of fields in the trajectory.

Returns
A new Trajectory.