old_contents: # Copyright 2017 reinforce.io. 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.
  # ==============================================================================
  
  from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  
  import unittest
  
  from tensorforce.tests.base_agent_test import BaseAgentTest
  from tensorforce.agents import DQNAgent
  
  
  class TestDQNAgent(BaseAgentTest, unittest.TestCase):
  
      agent = DQNAgent
      config = dict(
          update_mode=dict(
              unit='timesteps',
              batch_size=8,
              frequency=8
          ),
          # memory=dict(
          #     type='replay',
          #     include_next_states=True,
          #     capacity=100
          # ),
          memory=dict(
              type='prioritized_replay',
              include_next_states=True,
              buffer_size=100,
              capacity=1000
          ),
          optimizer=dict(
              type='adam',
              learning_rate=1e-2
          ),
          states_preprocessing=[
              dict(type='running_standardize'),
              dict(type='sequence')
          ],
          target_sync_frequency=10,
          # Comment in to test exploration types
          # actions_exploration_spec=dict(
          #     type="epsilon_decay",
          #     initial_epsilon=1.0,
          #     final_epsilon=0.1,
          #     timesteps=10
          # ),
          # actions_exploration_spec=dict(
          #     type="epsilon_anneal",
          #     initial_epsilon=1.0,
          #     final_epsilon=0.1,
          #     timesteps=10
          # )
      )
  
      exclude_float = True
      exclude_bounded = True
  
new_contents: # Copyright 2017 reinforce.io. 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.
  # ==============================================================================
  
  from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  
  import unittest
  
  from tensorforce.tests.base_agent_test import BaseAgentTest
  from tensorforce.agents import DQNAgent
  
  
  class TestDQNAgent(BaseAgentTest, unittest.TestCase):
  
      agent = DQNAgent
      config = dict(
          update_mode=dict(
              unit='timesteps',
              batch_size=8,
              frequency=8
          ),
          memory=dict(
              type='replay',
              include_next_states=True,
              capacity=100
          ),
          # memory=dict(
          #     type='prioritized_replay',
          #     include_next_states=True,
          #     buffer_size=100,
          #     capacity=1000
          # ),
          optimizer=dict(
              type='adam',
              learning_rate=1e-2
          ),
          states_preprocessing=[
              dict(type='running_standardize'),
              dict(type='sequence')
          ],
          target_sync_frequency=10,
          # Comment in to test exploration types
          # actions_exploration_spec=dict(
          #     type="epsilon_decay",
          #     initial_epsilon=1.0,
          #     final_epsilon=0.1,
          #     timesteps=10
          # ),
          # actions_exploration_spec=dict(
          #     type="epsilon_anneal",
          #     initial_epsilon=1.0,
          #     final_epsilon=0.1,
          #     timesteps=10
          # )
      )
  
      exclude_float = True
      exclude_bounded = True
  
subject: Use replay, not prioritized replay in dqn test.