text: #  The MIT License (MIT)
  #
  #  Copyright (c) 2017 Yu Ishihara
  #
  #  Permission is hereby granted, free of charge, to any person obtaining a copy
  #  of this software and associated documentation files (the "Software"), to deal
  #  in the Software without restriction, including without limitation the rights
  #  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  #  copies of the Software, and to permit persons to whom the Software is
  #  furnished to do so, subject to the following conditions:
  #
  #  The above copyright notice and this permission notice shall be included in
  #  all copies or substantial portions of the Software.
  #
  #  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  #  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  #  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  #  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  #  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  #  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  #  THE SOFTWARE.
  
  from a3c_network import A3CNetwork
  
  import tensorflow as tf
  
  class SharedNetwork(A3CNetwork):
    def __init__(self, image_height, image_width, num_channels, num_actions, thread_id, local_t_max, global_t_max, device='/cpu:0'):
      super(SharedNetwork, self).__init__(image_height, image_width, num_channels, num_actions, thread_id, device)
      scope_name = "a3c_network_%d" % thread_id
      with tf.variable_scope(scope_name):
        self.eta = 7e-4
        self.alpha = 0.99
        self.momentum = 0.0
        self.epsilon = 0.1
        self.local_t_max = local_t_max
        self.global_t_max = global_t_max
        self.shared_counter = self.prepare_shared_counter()
        self.learning_rate = self.learning_rate()
        self.optimizer = self.prepare_optimizer(self.learning_rate)
  
  
    def prepare_optimizer(self, learning_rate):
      with tf.device(self.device):
        return tf.train.RMSPropOptimizer(
            learning_rate=learning_rate,
            decay=self.alpha,
            momentum=self.momentum,
            epsilon=self.epsilon,
            name="shared_optimizer")
  
  
    def learning_rate(self):
      return self.eta * (1.0 - self.shared_counter.ref() * self.local_t_max / self.global_t_max)
  
  
    def prepare_shared_counter(self):
      return tf.get_variable('counter', [], initializer=tf.constant_initializer(0), trainable=False)
  