text: #   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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 parl
  import paddle.fluid as fluid
  from parl import layers
  
  
  class AtariModel(parl.Model):
      def __init__(self, act_dim):
  
          self.conv1 = layers.conv2d(
              num_filters=32, filter_size=8, stride=4, padding=1, act='relu')
          self.conv2 = layers.conv2d(
              num_filters=64, filter_size=4, stride=2, padding=2, act='relu')
          self.conv3 = layers.conv2d(
              num_filters=64, filter_size=3, stride=1, padding=0, act='relu')
  
          self.fc = layers.fc(size=512, act='relu')
  
          self.policy_fc = layers.fc(size=act_dim)
          self.value_fc = layers.fc(size=1)
  
      def policy(self, obs):
          """
          Args:
              obs: A float32 tensor of shape [B, C, H, W]
  
          Returns:
              policy_logits: B * ACT_DIM
          """
          obs = obs / 255.0
          conv1 = self.conv1(obs)
          conv2 = self.conv2(conv1)
          conv3 = self.conv3(conv2)
  
          flatten = layers.flatten(conv3, axis=1)
          fc_output = self.fc(flatten)
  
          policy_logits = self.policy_fc(fc_output)
          return policy_logits
  
      def value(self, obs):
          """
          Args:
              obs: A float32 tensor of shape [B, C, H, W]
  
          Returns:
              values: B
          """
          obs = obs / 255.0
          conv1 = self.conv1(obs)
          conv2 = self.conv2(conv1)
          conv3 = self.conv3(conv2)
  
          flatten = layers.flatten(conv3, axis=1)
          fc_output = self.fc(flatten)
  
          values = self.value_fc(fc_output)
          values = layers.squeeze(values, axes=[1])
          return values
  
      def policy_and_value(self, obs):
          """
          Args:
              obs: A float32 tensor of shape [B, C, H, W]
  
          Returns:
              policy_logits: B * ACT_DIM
              values: B
          """
          obs = obs / 255.0
          conv1 = self.conv1(obs)
          conv2 = self.conv2(conv1)
          conv3 = self.conv3(conv2)
  
          flatten = layers.flatten(conv3, axis=1)
          fc_output = self.fc(flatten)
  
          policy_logits = self.policy_fc(fc_output)
  
          values = self.value_fc(fc_output)
          values = layers.squeeze(values, axes=[1])
  
          return policy_logits, values
  