text: # coding=utf-8
  # Copyright 2018 The TF-Agents Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  # Lint as: python2, python3
  r"""Train and Eval DDPG.
  
  To run:
  
  ```bash
  tensorboard --logdir $HOME/tmp/ddpg_rnn_v1/dm/CartPole-Balance/ --port 2223 &
  
  python tf_agents/agents/ddpg/examples/v1/train_eval_rnn.py \
    --root_dir=$HOME/tmp/ddpg_rnn_v1/dm/CartPole-Balance/ \
    --num_iterations=100000 \
    --alsologtostderr
  ```
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import functools
  import os
  import time
  
  from absl import app
  from absl import flags
  from absl import logging
  
  import gin
  from six.moves import range
  import tensorflow as tf  # pylint: disable=g-explicit-tensorflow-version-import
  
  from tf_agents.agents.ddpg import actor_rnn_network
  from tf_agents.agents.ddpg import critic_rnn_network
  from tf_agents.agents.ddpg import ddpg_agent
  from tf_agents.drivers import dynamic_episode_driver
  from tf_agents.environments import suite_dm_control
  from tf_agents.environments import tf_py_environment
  from tf_agents.environments import wrappers
  from tf_agents.eval import metric_utils
  from tf_agents.metrics import py_metrics
  from tf_agents.metrics import tf_metrics
  from tf_agents.policies import py_tf_policy
  from tf_agents.replay_buffers import tf_uniform_replay_buffer
  from tf_agents.utils import common
  
  
  flags.DEFINE_string('root_dir', os.getenv('TEST_UNDECLARED_OUTPUTS_DIR'),
                      'Root directory for writing logs/summaries/checkpoints.')
  flags.DEFINE_integer('num_iterations', 100000,
                       'Total number train/eval iterations to perform.')
  FLAGS = flags.FLAGS
  
  
  @gin.configurable
  def train_eval(
      root_dir,
      env_name='cartpole',
      task_name='balance',
      observations_whitelist='position',
      num_iterations=100000,
      actor_fc_layers=(400, 300),
      actor_output_fc_layers=(100,),
      actor_lstm_size=(40,),
      critic_obs_fc_layers=(400,),
      critic_action_fc_layers=None,
      critic_joint_fc_layers=(300,),
      critic_output_fc_layers=(100,),
      critic_lstm_size=(40,),
      # Params for collect
      initial_collect_steps=1,
      collect_episodes_per_iteration=1,
      replay_buffer_capacity=100000,
      ou_stddev=0.2,
      ou_damping=0.15,
      # Params for target update
      target_update_tau=0.05,
      target_update_period=5,
      # Params for train
      train_steps_per_iteration=200,
      batch_size=64,
      train_sequence_length=10,
      actor_learning_rate=1e-4,
      critic_learning_rate=1e-3,
      dqda_clipping=None,
      gamma=0.995,
      reward_scale_factor=1.0,
      gradient_clipping=None,
      # Params for eval
      num_eval_episodes=10,
      eval_interval=1000,
      # Params for checkpoints, summaries, and logging
      train_checkpoint_interval=10000,
      policy_checkpoint_interval=5000,
      rb_checkpoint_interval=10000,
      log_interval=1000,
      summary_interval=1000,
      summaries_flush_secs=10,
      debug_summaries=False,
      summarize_grads_and_vars=False,
      eval_metrics_callback=None):
  
    """A simple train and eval for DDPG."""
    root_dir = os.path.expanduser(root_dir)
    train_dir = os.path.join(root_dir, 'train')
    eval_dir = os.path.join(root_dir, 'eval')
  
    train_summary_writer = tf.compat.v2.summary.create_file_writer(
        train_dir, flush_millis=summaries_flush_secs * 1000)
    train_summary_writer.set_as_default()
  
    eval_summary_writer = tf.compat.v2.summary.create_file_writer(
        eval_dir, flush_millis=summaries_flush_secs * 1000)
    eval_metrics = [
        py_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
        py_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes),
    ]
  
    global_step = tf.compat.v1.train.get_or_create_global_step()
    with tf.compat.v2.summary.record_if(
        lambda: tf.math.equal(global_step % summary_interval, 0)):
      if observations_whitelist is not None:
        env_wrappers = [
            functools.partial(
                wrappers.FlattenObservationsWrapper,
                observations_whitelist=[observations_whitelist])
        ]
      else:
        env_wrappers = []
      environment = suite_dm_control.load(
          env_name, task_name, env_wrappers=env_wrappers)
  
      tf_env = tf_py_environment.TFPyEnvironment(environment)
      eval_py_env = suite_dm_control.load(
          env_name, task_name, env_wrappers=env_wrappers)
  
      actor_net = actor_rnn_network.ActorRnnNetwork(
          tf_env.time_step_spec().observation,
          tf_env.action_spec(),
          input_fc_layer_params=actor_fc_layers,
          lstm_size=actor_lstm_size,
          output_fc_layer_params=actor_output_fc_layers)
  
      critic_net_input_specs = (tf_env.time_step_spec().observation,
                                tf_env.action_spec())
  
      critic_net = critic_rnn_network.CriticRnnNetwork(
          critic_net_input_specs,
          observation_fc_layer_params=critic_obs_fc_layers,
          action_fc_layer_params=critic_action_fc_layers,
          joint_fc_layer_params=critic_joint_fc_layers,
          lstm_size=critic_lstm_size,
          output_fc_layer_params=critic_output_fc_layers,
      )
  
      tf_agent = ddpg_agent.DdpgAgent(
          tf_env.time_step_spec(),
          tf_env.action_spec(),
          actor_network=actor_net,
          critic_network=critic_net,
          actor_optimizer=tf.compat.v1.train.AdamOptimizer(
              learning_rate=actor_learning_rate),
          critic_optimizer=tf.compat.v1.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,
          gamma=gamma,
          reward_scale_factor=reward_scale_factor,
          gradient_clipping=gradient_clipping,
          debug_summaries=debug_summaries,
          summarize_grads_and_vars=summarize_grads_and_vars,
          train_step_counter=global_step)
  
      replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
          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(),
      ]
  
      collect_policy = tf_agent.collect_policy
  
      initial_collect_op = dynamic_episode_driver.DynamicEpisodeDriver(
          tf_env,
          collect_policy,
          observers=[replay_buffer.add_batch] + train_metrics,
          num_episodes=initial_collect_steps).run()
  
      collect_op = dynamic_episode_driver.DynamicEpisodeDriver(
          tf_env,
          collect_policy,
          observers=[replay_buffer.add_batch] + train_metrics,
          num_episodes=collect_episodes_per_iteration).run()
  
      # Need extra step to generate transitions of train_sequence_length.
      # Dataset generates trajectories with shape [BxTx...]
      dataset = replay_buffer.as_dataset(
          num_parallel_calls=3,
          sample_batch_size=batch_size,
          num_steps=train_sequence_length + 1).prefetch(3)
  
      iterator = tf.compat.v1.data.make_initializable_iterator(dataset)
      trajectories, unused_info = iterator.get_next()
  
      train_fn = common.function(tf_agent.train)
      train_op = train_fn(experience=trajectories)
  
      train_checkpointer = common.Checkpointer(
          ckpt_dir=train_dir,
          agent=tf_agent,
          global_step=global_step,
          metrics=metric_utils.MetricsGroup(train_metrics, 'train_metrics'))
      policy_checkpointer = common.Checkpointer(
          ckpt_dir=os.path.join(train_dir, 'policy'),
          policy=tf_agent.policy,
          global_step=global_step)
      rb_checkpointer = common.Checkpointer(
          ckpt_dir=os.path.join(train_dir, 'replay_buffer'),
          max_to_keep=1,
          replay_buffer=replay_buffer)
  
      summary_ops = []
      for train_metric in train_metrics:
        summary_ops.append(train_metric.tf_summaries(
            train_step=global_step, step_metrics=train_metrics[:2]))
  
      with eval_summary_writer.as_default(), \
           tf.compat.v2.summary.record_if(True):
        for eval_metric in eval_metrics:
          eval_metric.tf_summaries(train_step=global_step)
  
      init_agent_op = tf_agent.initialize()
  
      with tf.compat.v1.Session() as sess:
        # Initialize the graph.
        train_checkpointer.initialize_or_restore(sess)
        rb_checkpointer.initialize_or_restore(sess)
        sess.run(iterator.initializer)
        # TODO(b/126239733) Remove once Periodically can be saved.
        common.initialize_uninitialized_variables(sess)
  
        sess.run(init_agent_op)
        sess.run(train_summary_writer.init())
        sess.run(eval_summary_writer.init())
        sess.run(initial_collect_op)
  
        global_step_val = sess.run(global_step)
        metric_utils.compute_summaries(
            eval_metrics,
            eval_py_env,
            eval_py_policy,
            num_episodes=num_eval_episodes,
            global_step=global_step_val,
            callback=eval_metrics_callback,
            log=True,
        )
  
        collect_call = sess.make_callable(collect_op)
        train_step_call = sess.make_callable([train_op, summary_ops])
        global_step_call = sess.make_callable(global_step)
  
        timed_at_step = global_step_call()
        time_acc = 0
        steps_per_second_ph = tf.compat.v1.placeholder(
            tf.float32, shape=(), name='steps_per_sec_ph')
        steps_per_second_summary = tf.compat.v2.summary.scalar(
            name='global_steps_per_sec', data=steps_per_second_ph,
            step=global_step)
  
        for _ in range(num_iterations):
          start_time = time.time()
          collect_call()
          for _ in range(train_steps_per_iteration):
            loss_info_value, _ = train_step_call()
            global_step_val = global_step_call()
          time_acc += time.time() - start_time
  
          if global_step_val % log_interval == 0:
            logging.info('step = %d, loss = %f', global_step_val,
                         loss_info_value.loss)
            steps_per_sec = (global_step_val - timed_at_step) / time_acc
            logging.info('%.3f steps/sec', steps_per_sec)
            sess.run(
                steps_per_second_summary,
                feed_dict={steps_per_second_ph: steps_per_sec})
            timed_at_step = global_step_val
            time_acc = 0
  
          if global_step_val % train_checkpoint_interval == 0:
            train_checkpointer.save(global_step=global_step_val)
  
          if global_step_val % policy_checkpoint_interval == 0:
            policy_checkpointer.save(global_step=global_step_val)
  
          if global_step_val % rb_checkpoint_interval == 0:
            rb_checkpointer.save(global_step=global_step_val)
  
          if global_step_val % eval_interval == 0:
            metric_utils.compute_summaries(
                eval_metrics,
                eval_py_env,
                eval_py_policy,
                num_episodes=num_eval_episodes,
                global_step=global_step_val,
                callback=eval_metrics_callback,
                log=True
            )
  
  
  def main(_):
    logging.set_verbosity(logging.INFO)
    tf.compat.v1.enable_resource_variables()
    train_eval(FLAGS.root_dir, num_iterations=FLAGS.num_iterations)
  
  
  if __name__ == '__main__':
    flags.mark_flag_as_required('root_dir')
    app.run(main)
  