text: # 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
      deterministic = True
  
      config = dict(
          memory=dict(
              type='replay',
              capacity=1000
          ),
          optimizer=dict(
              type="adam",
              learning_rate=0.002
          ),
          # Comment in to test exploration types
          # explorations_spec=dict(
          #     type="epsilon_decay",
          #     initial_epsilon=1.0,
          #     final_epsilon=0.1,
          #     timesteps=10
          # ),
          # explorations_spec=dict(
          #     type="epsilon_anneal",
          #     initial_epsilon=1.0,
          #     final_epsilon=0.1,
          #     timesteps=10
          # ),
          repeat_update=4,
          batch_size=32,
          first_update=64,
          target_sync_frequency=10
      )
  
      exclude_float = True
      exclude_bounded = True
  
      multi_config = dict(
          memory=dict(
              type='replay',
              capacity=1000
          ),
          optimizer=dict(
              type="adam",
              learning_rate=0.01
          ),
          repeat_update=1,
          batch_size=16,
          first_update=16,
          target_sync_frequency=10
      )
  