old_contents: #
  # 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
  
  from rl_coach.architectures.tensorflow_components.layers import Dense
  from rl_coach.architectures.tensorflow_components.heads.head import Head
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.core_types import QActionStateValue
  from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace
  
  
  class QHead(Head):
      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 = 'q_values_head'
          if isinstance(self.spaces.action, BoxActionSpace):
              self.num_actions = 1
          elif isinstance(self.spaces.action, DiscreteActionSpace):
              self.num_actions = len(self.spaces.action.actions)
          self.return_type = QActionStateValue
          if agent_parameters.network_wrappers[self.network_name].replace_mse_with_huber_loss:
              self.loss_type = tf.losses.huber_loss
          else:
              self.loss_type = tf.losses.mean_squared_error
  
      def _build_module(self, input_layer):
          # Standard Q Network
          self.output = self.dense_layer(self.num_actions)(input_layer, name='output')
  
      def __str__(self):
          result = [
              "Dense (num outputs = {})".format(self.num_actions)
          ]
          return '\n'.join(result)
  
  
  
new_contents: #
  # 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
  
  from rl_coach.architectures.tensorflow_components.layers import Dense
  from rl_coach.architectures.tensorflow_components.heads.head import Head
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.core_types import QActionStateValue
  from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace
  
  
  class QHead(Head):
      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 = 'q_values_head'
          if isinstance(self.spaces.action, BoxActionSpace):
              self.num_actions = 1
          elif isinstance(self.spaces.action, DiscreteActionSpace):
              self.num_actions = len(self.spaces.action.actions)
          else:
              raise ValueError(
                  'QHead does not support action spaces of type: {class_name}'.format(
                      class_name=self.spaces.action.__class__.__name__,
                  )
              )
          self.return_type = QActionStateValue
          if agent_parameters.network_wrappers[self.network_name].replace_mse_with_huber_loss:
              self.loss_type = tf.losses.huber_loss
          else:
              self.loss_type = tf.losses.mean_squared_error
  
      def _build_module(self, input_layer):
          # Standard Q Network
          self.output = self.dense_layer(self.num_actions)(input_layer, name='output')
  
      def __str__(self):
          result = [
              "Dense (num outputs = {})".format(self.num_actions)
          ]
          return '\n'.join(result)
  
subject: add meaningful error message in the event that the action space is not one that can be used (#151)