text: #
  # Copyright (c) 2017 Intel Corporation
  #
  # 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.
  #
  
  import tensorflow as tf
  import numpy as np
  from rl_coach.architectures.tensorflow_components.heads import QHead
  from rl_coach.architectures.tensorflow_components.layers import Dense
  
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.spaces import SpacesDefinition
  
  
  class CategoricalQHead(QHead):
      def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
                   head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str ='relu',
                   dense_layer=Dense):
          super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function,
                           dense_layer=dense_layer)
          self.name = 'categorical_dqn_head'
          self.num_actions = len(self.spaces.action.actions)
          self.num_atoms = agent_parameters.algorithm.atoms
          self.z_values = tf.cast(tf.constant(np.linspace(self.ap.algorithm.v_min, self.ap.algorithm.v_max,
                                                          self.ap.algorithm.atoms), dtype=tf.float32), dtype=tf.float64)
          self.loss_type = []
  
      def _build_module(self, input_layer):
          values_distribution = self.dense_layer(self.num_actions * self.num_atoms)(input_layer, name='output')
          values_distribution = tf.reshape(values_distribution, (tf.shape(values_distribution)[0], self.num_actions,
                                                                 self.num_atoms))
          # softmax on atoms dimension
          self.output = tf.nn.softmax(values_distribution)
  
          # calculate cross entropy loss
          self.distributions = tf.placeholder(tf.float32, shape=(None, self.num_actions, self.num_atoms),
                                              name="distributions")
          self.target = self.distributions
          self.loss = tf.nn.softmax_cross_entropy_with_logits(labels=self.target, logits=values_distribution)
          tf.losses.add_loss(self.loss)
  
          self.q_values = tf.tensordot(tf.cast(self.output, tf.float64), self.z_values, 1)
  
          # used in batch-rl to estimate a probablity distribution over actions
          self.softmax = self.add_softmax_with_temperature()
  
      def __str__(self):
          result = [
              "Dense (num outputs = {})".format(self.num_actions * self.num_atoms),
              "Reshape (output size = {} x {})".format(self.num_actions, self.num_atoms),
              "Softmax"
          ]
          return '\n'.join(result)
  
  