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A driver that takes N steps in an environment using a tf.while_loop.

Inherits From: Driver

    *args, **kwargs

Used in the notebooks

Used in the tutorials

The while loop will run num_steps in the environment, only counting steps that result in an environment transition, i.e. (time_step, action, next_time_step). If a step results in environment resetting, i.e. time_step.is_last() and next_time_step.is_first() (traj.is_boundary()), this is not counted toward the num_steps.

As environments run batched time_steps, the counters for all batch elements are summed, and execution stops when the total exceeds num_steps. When batch_size > 1, there is no guarantee that exactly num_steps are taken -- it may be more but never less.

This termination condition can be overridden in subclasses by implementing the self._loop_condition_fn() method.


  • env: A tf_environment.Base environment.
  • policy: A tf_policy.Base policy.
  • observers: A list of observers that are updated after every step in the environment. Each observer is a callable(time_step.Trajectory).
  • transition_observers: A list of observers that are updated after every step in the environment. Each observer is a callable((TimeStep, PolicyStep, NextTimeStep)).
  • num_steps: The number of steps to take in the environment.


  • env
  • observers
  • policy
  • transition_observers


  • ValueError: If env is not a tf_environment.Base or policy is not an instance of tf_policy.Base.



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    time_step=None, policy_state=None, maximum_iterations=None

Takes steps in the environment using the policy while updating observers.


  • time_step: optional initial time_step. If None, it will use the current_time_step of the environment. Elements should be shape [batch_size, ...].
  • policy_state: optional initial state for the policy.
  • maximum_iterations: Optional maximum number of iterations of the while loop to run. If provided, the cond output is AND-ed with an additional condition ensuring the number of iterations executed is no greater than maximum_iterations.


  • time_step: TimeStep named tuple with final observation, reward, etc.
  • policy_state: Tensor with final step policy state.