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.
  
  r"""Train and Eval TD3.
  
  To run:
  
  ```bash
  tensorboard --logdir $HOME/tmp/td3/gym/HalfCheetah-v2/ --port 2223 &
  
  python tf_agents/agents/td3/examples/v2/train_eval.py \
    --root_dir=$HOME/tmp/td3/gym/HalfCheetah-v2/ \
    --num_iterations=2000000 \
    --alsologtostderr
  ```
  
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import time
  
  from absl import app
  from absl import flags
  from absl import logging
  
  import gin
  import tensorflow as tf
  
  from tf_agents.agents.ddpg import actor_network
  from tf_agents.agents.ddpg import critic_network
  from tf_agents.agents.td3 import td3_agent
  from tf_agents.drivers import dynamic_step_driver
  from tf_agents.environments import suite_mujoco
  from tf_agents.environments import tf_py_environment
  from tf_agents.eval import metric_utils
  from tf_agents.metrics import tf_metrics
  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.DEFINE_multi_string('gin_file', None, 'Paths to the gin-config files.')
  flags.DEFINE_multi_string('gin_param', None, 'Gin binding parameters.')
  
  FLAGS = flags.FLAGS
  
  
  @gin.configurable
  def train_eval(
      root_dir,
      env_name='HalfCheetah-v2',
      num_iterations=2000000,
      actor_fc_layers=(400, 300),
      critic_obs_fc_layers=(400,),
      critic_action_fc_layers=None,
      critic_joint_fc_layers=(300,),
      # Params for collect
      initial_collect_steps=1000,
      collect_steps_per_iteration=1,
      replay_buffer_capacity=100000,
      exploration_noise_std=0.1,
      # Params for target update
      target_update_tau=0.05,
      target_update_period=5,
      # Params for train
      train_steps_per_iteration=1,
      batch_size=64,
      actor_update_period=2,
      actor_learning_rate=1e-4,
      critic_learning_rate=1e-3,
      dqda_clipping=None,
      td_errors_loss_fn=tf.compat.v1.losses.huber_loss,
      gamma=0.995,
      reward_scale_factor=1.0,
      gradient_clipping=None,
      use_tf_functions=True,
      # Params for eval
      num_eval_episodes=10,
      eval_interval=10000,
      # Params for checkpoints, summaries, and logging
      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 TD3."""
    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 = [
        tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
        tf_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)):
      tf_env = tf_py_environment.TFPyEnvironment(suite_mujoco.load(env_name))
      eval_tf_env = tf_py_environment.TFPyEnvironment(suite_mujoco.load(env_name))
  
      actor_net = actor_network.ActorNetwork(
          tf_env.time_step_spec().observation,
          tf_env.action_spec(),
          fc_layer_params=actor_fc_layers,
      )
  
      critic_net_input_specs = (tf_env.time_step_spec().observation,
                                tf_env.action_spec())
  
      critic_net = critic_network.CriticNetwork(
          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,
      )
  
      tf_agent = td3_agent.Td3Agent(
          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),
          exploration_noise_std=exploration_noise_std,
          target_update_tau=target_update_tau,
          target_update_period=target_update_period,
          actor_update_period=actor_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,
          train_step_counter=global_step,
      )
      tf_agent.initialize()
  
      train_metrics = [
          tf_metrics.NumberOfEpisodes(),
          tf_metrics.EnvironmentSteps(),
          tf_metrics.AverageReturnMetric(),
          tf_metrics.AverageEpisodeLengthMetric(),
      ]
  
      eval_policy = tf_agent.policy
      collect_policy = tf_agent.collect_policy
  
      replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
          tf_agent.collect_data_spec,
          batch_size=tf_env.batch_size,
          max_length=replay_buffer_capacity)
  
      initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
          tf_env,
          collect_policy,
          observers=[replay_buffer.add_batch],
          num_steps=initial_collect_steps)
  
      collect_driver = dynamic_step_driver.DynamicStepDriver(
          tf_env,
          collect_policy,
          observers=[replay_buffer.add_batch] + train_metrics,
          num_steps=collect_steps_per_iteration)
  
      if use_tf_functions:
        initial_collect_driver.run = common.function(initial_collect_driver.run)
        collect_driver.run = common.function(collect_driver.run)
        tf_agent.train = common.function(tf_agent.train)
  
      # Collect initial replay data.
      logging.info(
          'Initializing replay buffer by collecting experience for %d steps with '
          'a random policy.', initial_collect_steps)
      initial_collect_driver.run()
  
      results = metric_utils.eager_compute(
          eval_metrics,
          eval_tf_env,
          eval_policy,
          num_episodes=num_eval_episodes,
          train_step=global_step,
          summary_writer=eval_summary_writer,
          summary_prefix='Metrics',
      )
      if eval_metrics_callback is not None:
        eval_metrics_callback(results, global_step.numpy())
      metric_utils.log_metrics(eval_metrics)
  
      time_step = None
      policy_state = collect_policy.get_initial_state(tf_env.batch_size)
  
      timed_at_step = global_step.numpy()
      time_acc = 0
  
      # Dataset generates trajectories with shape [Bx2x...]
      dataset = replay_buffer.as_dataset(
          num_parallel_calls=3,
          sample_batch_size=batch_size,
          num_steps=2).prefetch(3)
      iterator = iter(dataset)
  
      for _ in range(num_iterations):
        start_time = time.time()
        time_step, policy_state = collect_driver.run(
            time_step=time_step,
            policy_state=policy_state,
        )
        for _ in range(train_steps_per_iteration):
          experience, _ = next(iterator)
          train_loss = tf_agent.train(experience)
        time_acc += time.time() - start_time
  
        if global_step.numpy() % log_interval == 0:
          logging.info('step = %d, loss = %f', global_step.numpy(),
                       train_loss.loss)
          steps_per_sec = (global_step.numpy() - timed_at_step) / time_acc
          logging.info('%.3f steps/sec', steps_per_sec)
          tf.compat.v2.summary.scalar(
              name='global_steps_per_sec', data=steps_per_sec, step=global_step)
          timed_at_step = global_step.numpy()
          time_acc = 0
  
        for train_metric in train_metrics:
          train_metric.tf_summaries(
              train_step=global_step, step_metrics=train_metrics[:2])
  
        if global_step.numpy() % eval_interval == 0:
          results = metric_utils.eager_compute(
              eval_metrics,
              eval_tf_env,
              eval_policy,
              num_episodes=num_eval_episodes,
              train_step=global_step,
              summary_writer=eval_summary_writer,
              summary_prefix='Metrics',
          )
          if eval_metrics_callback is not None:
            eval_metrics_callback(results, global_step.numpy())
          metric_utils.log_metrics(eval_metrics)
  
      return train_loss
  
  
  def main(_):
    tf.compat.v1.enable_v2_behavior()
    logging.set_verbosity(logging.INFO)
    gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_param)
    train_eval(FLAGS.root_dir, num_iterations=FLAGS.num_iterations)
  
  
  if __name__ == '__main__':
    flags.mark_flag_as_required('root_dir')
    app.run(main)
  