46350367a2cda5204cb37f2289021d14064d2d7d,tf_agents/agents/ddpg/examples/train_eval_mujoco.py,,train_eval,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,63

Before Change

        target_update_period=target_update_period,
        actor_optimizer=tf.train.AdamOptimizer(
            learning_rate=actor_learning_rate),
        critic_optimizer=tf.train.AdamOptimizer(
            learning_rate=critic_learning_rate),
        train_batch_size=batch_size,
        dqda_clipping=dqda_clipping,
        td_errors_loss_fn=td_errors_loss_fn,

After Change

        critic_net=critic_net,
        actor_optimizer=tf.train.AdamOptimizer(
            learning_rate=actor_learning_rate),
        critic_optimizer=tf.train.AdamOptimizer(
            learning_rate=critic_learning_rate),
        ou_stddev=ou_stddev,
        ou_damping=ou_damping,
        target_update_tau=target_update_tau,
        target_update_period=target_update_period,
        dqda_clipping=dqda_clipping,
        td_errors_loss_fn=td_errors_loss_fn,
        gamma=gamma,
        reward_scale_factor=reward_scale_factor,
        gradient_clipping=gradient_clipping,
        debug_summaries=debug_summaries,
        summarize_grads_and_vars=summarize_grads_and_vars)

    replay_buffer = batched_replay_buffer.BatchedReplayBuffer(
        tf_agent.collect_data_spec(),
        batch_size=tf_env.batch_size,
        max_length=replay_buffer_capacity)

    eval_py_policy = py_tf_policy.PyTFPolicy(tf_agent.policy())

    train_metrics = [
        tf_metrics.NumberOfEpisodes(),
        tf_metrics.EnvironmentSteps(),
        tf_metrics.AverageReturnMetric(),
        tf_metrics.AverageEpisodeLengthMetric(),
    ]

    global_step = tf.train.get_or_create_global_step()

    collect_policy = tf_agent.collect_policy()
    initial_collect_op = dynamic_step_driver.DynamicStepDriver(
        tf_env,
        collect_policy,
        observers=[replay_buffer.add_batch],
        num_steps=initial_collect_steps).run()

    collect_op = dynamic_step_driver.DynamicStepDriver(
        tf_env,
        collect_policy,
        observers=[replay_buffer.add_batch] + train_metrics,
        num_steps=collect_steps_per_iteration).run()

    // Dataset generates trajectories with shape [Bx2x...]
    dataset = replay_buffer.as_dataset(
        num_parallel_calls=3,
        sample_batch_size=batch_size,
        num_steps=2,
        time_stacked=True).prefetch(3)

    iterator = dataset.make_initializable_iterator()
    trajectories, unused_ids, unused_probs = iterator.get_next()
    train_op = tf_agent.train(
        experience=trajectories, train_step_counter=global_step)

    train_checkpointer = common_utils.Checkpointer(

Instances