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A driver that takes N episodes 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_episodes in the environment, counting transitions that result in ending an episode.

As environments run batched time_episodes, the counters for all batch elements are summed, and execution stops when the total exceeds num_episodes.

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(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_episodes: The number of episodes 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, num_episodes=None, maximum_iterations=None

Takes episodes in the environment using the policy and update observers.

If time_step and policy_state are not provided, run will reset the environment and request an initial state from the policy.


  • time_step: optional initial time_step. If None, it will be obtained by resetting the environment. Elements should be shape [batch_size, ...].
  • policy_state: optional initial state for the policy. If None, it will be obtained from the policy.get_initial_state().
  • num_episodes: Optional number of episodes to take in the environment. If None it would use initial num_episodes.
  • 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.