code: import numpy as np
  import unittest
  
  import ray
  import ray.rllib.agents.dqn as dqn
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.test_utils import check, framework_iterator, \
      check_compute_action
  
  tf = try_import_tf()
  
  
  class TestDQN(unittest.TestCase):
      @classmethod
      def setUpClass(cls) -> None:
          ray.init()
  
      @classmethod
      def tearDownClass(cls) -> None:
          ray.shutdown()
  
      def test_dqn_compilation(self):
          """Test whether a DQNTrainer can be built on all frameworks."""
          config = dqn.DEFAULT_CONFIG.copy()
          config["num_workers"] = 2
          num_iterations = 1
  
          for fw in framework_iterator(config):
              # Double-dueling DQN.
              plain_config = config.copy()
              trainer = dqn.DQNTrainer(config=plain_config, env="CartPole-v0")
              for i in range(num_iterations):
                  results = trainer.train()
                  print(results)
  
              check_compute_action(trainer)
  
              # Rainbow.
              # TODO(sven): Add torch once DQN-torch supports distributional-Q.
              if fw == "torch":
                  continue
              rainbow_config = config.copy()
              rainbow_config["num_atoms"] = 10
              rainbow_config["noisy"] = True
              rainbow_config["double_q"] = True
              rainbow_config["dueling"] = True
              rainbow_config["n_step"] = 5
              trainer = dqn.DQNTrainer(config=rainbow_config, env="CartPole-v0")
              for i in range(num_iterations):
                  results = trainer.train()
                  print(results)
  
              check_compute_action(trainer)
  
      def test_dqn_exploration_and_soft_q_config(self):
          """Tests, whether a DQN Agent outputs exploration/softmaxed actions."""
          config = dqn.DEFAULT_CONFIG.copy()
          config["num_workers"] = 0  # Run locally.
          config["env_config"] = {"is_slippery": False, "map_name": "4x4"}
          obs = np.array(0)
  
          # Test against all frameworks.
          for _ in framework_iterator(config):
              # Default EpsilonGreedy setup.
              trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
              # Setting explore=False should always return the same action.
              a_ = trainer.compute_action(obs, explore=False)
              for _ in range(50):
                  a = trainer.compute_action(obs, explore=False)
                  check(a, a_)
              # explore=None (default: explore) should return different actions.
              actions = []
              for _ in range(50):
                  actions.append(trainer.compute_action(obs))
              check(np.std(actions), 0.0, false=True)
  
              # Low softmax temperature. Behaves like argmax
              # (but no epsilon exploration).
              config["exploration_config"] = {
                  "type": "SoftQ",
                  "temperature": 0.000001
              }
              trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
              # Due to the low temp, always expect the same action.
              actions = [trainer.compute_action(obs)]
              for _ in range(50):
                  actions.append(trainer.compute_action(obs))
              check(np.std(actions), 0.0, decimals=3)
  
              # Higher softmax temperature.
              config["exploration_config"]["temperature"] = 1.0
              trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
  
              # Even with the higher temperature, if we set explore=False, we
              # should expect the same actions always.
              a_ = trainer.compute_action(obs, explore=False)
              for _ in range(50):
                  a = trainer.compute_action(obs, explore=False)
                  check(a, a_)
  
              # Due to the higher temp, expect different actions avg'ing
              # around 1.5.
              actions = []
              for _ in range(300):
                  actions.append(trainer.compute_action(obs))
              check(np.std(actions), 0.0, false=True)
  
              # With Random exploration.
              config["exploration_config"] = {"type": "Random"}
              config["explore"] = True
              trainer = dqn.DQNTrainer(config=config, env="FrozenLake-v0")
              actions = []
              for _ in range(300):
                  actions.append(trainer.compute_action(obs))
              check(np.std(actions), 0.0, false=True)
  
  
  if __name__ == "__main__":
      import pytest
      import sys
      sys.exit(pytest.main(["-v", __file__]))
  
apis: numpy.array numpy.std