|View source on GitHub|
A driver that takes N episodes in an environment using a tf.while_loop.
tf_agents.drivers.dynamic_episode_driver.DynamicEpisodeDriver( *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.
ValueError: If env is not a tf_environment.Base or policy is not an instance of tf_policy.Base.
run( time_step=None, policy_state=None, num_episodes=None, maximum_iterations=None )
Takes episodes in the environment using the policy and update observers.
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.