text: import numpy as np
  import sys
  import unittest
  
  import ray
  import ray.rllib.agents.a3c as a3c
  import ray.rllib.agents.ddpg as ddpg
  import ray.rllib.agents.ddpg.td3 as td3
  import ray.rllib.agents.dqn as dqn
  import ray.rllib.agents.impala as impala
  import ray.rllib.agents.pg as pg
  import ray.rllib.agents.ppo as ppo
  import ray.rllib.agents.sac as sac
  from ray.rllib.utils import check, framework_iterator
  
  
  def do_test_explorations(run,
                           env,
                           config,
                           dummy_obs,
                           prev_a=None,
                           expected_mean_action=None):
      """Calls an Agent's `compute_actions` with different `explore` options."""
  
      core_config = config.copy()
      if run not in [a3c.A3CTrainer]:
          core_config["num_workers"] = 0
  
      # Test all frameworks.
      for _ in framework_iterator(core_config):
          print("Agent={}".format(run))
  
          # Test for both the default Agent's exploration AND the `Random`
          # exploration class.
          for exploration in [None, "Random"]:
              local_config = core_config.copy()
              if exploration == "Random":
                  # TODO(sven): Random doesn't work for IMPALA yet.
                  if run is impala.ImpalaTrainer:
                      continue
                  local_config["exploration_config"] = {"type": "Random"}
              print("exploration={}".format(exploration or "default"))
  
              trainer = run(config=local_config, env=env)
  
              # Make sure all actions drawn are the same, given same
              # observations.
              actions = []
              for _ in range(25):
                  actions.append(
                      trainer.compute_action(
                          observation=dummy_obs,
                          explore=False,
                          prev_action=prev_a,
                          prev_reward=1.0 if prev_a is not None else None))
                  check(actions[-1], actions[0])
  
              # Make sure actions drawn are different
              # (around some mean value), given constant observations.
              actions = []
              for _ in range(100):
                  actions.append(
                      trainer.compute_action(
                          observation=dummy_obs,
                          explore=True,
                          prev_action=prev_a,
                          prev_reward=1.0 if prev_a is not None else None))
              check(
                  np.mean(actions),
                  expected_mean_action
                  if expected_mean_action is not None else 0.5,
                  atol=0.3)
              # Check that the stddev is not 0.0 (values differ).
              check(np.std(actions), 0.0, false=True)
  
  
  class TestExplorations(unittest.TestCase):
      """
      Tests all Exploration components and the deterministic flag for
      compute_action calls.
      """
  
      @classmethod
      def setUpClass(cls):
          ray.init(ignore_reinit_error=True)
  
      @classmethod
      def tearDownClass(cls):
          ray.shutdown()
  
      def test_a2c(self):
          do_test_explorations(
              a3c.A2CTrainer,
              "CartPole-v0",
              a3c.DEFAULT_CONFIG,
              np.array([0.0, 0.1, 0.0, 0.0]),
              prev_a=np.array(1))
  
      def test_a3c(self):
          do_test_explorations(
              a3c.A3CTrainer,
              "CartPole-v0",
              a3c.DEFAULT_CONFIG,
              np.array([0.0, 0.1, 0.0, 0.0]),
              prev_a=np.array(1))
  
      def test_ddpg(self):
          # Switch off random timesteps at beginning. We want to test actual
          # GaussianNoise right away.
          config = ddpg.DEFAULT_CONFIG.copy()
          config["exploration_config"]["random_timesteps"] = 0
          do_test_explorations(
              ddpg.DDPGTrainer,
              "Pendulum-v0",
              config,
              np.array([0.0, 0.1, 0.0]),
              expected_mean_action=0.0)
  
      def test_simple_dqn(self):
          do_test_explorations(dqn.SimpleQTrainer, "CartPole-v0",
                               dqn.SIMPLE_Q_DEFAULT_CONFIG,
                               np.array([0.0, 0.1, 0.0, 0.0]))
  
      def test_dqn(self):
          do_test_explorations(dqn.DQNTrainer, "CartPole-v0", dqn.DEFAULT_CONFIG,
                               np.array([0.0, 0.1, 0.0, 0.0]))
  
      def test_impala(self):
          do_test_explorations(
              impala.ImpalaTrainer,
              "CartPole-v0",
              impala.DEFAULT_CONFIG,
              np.array([0.0, 0.1, 0.0, 0.0]),
              prev_a=np.array(0))
  
      def test_pg(self):
          do_test_explorations(
              pg.PGTrainer,
              "CartPole-v0",
              pg.DEFAULT_CONFIG,
              np.array([0.0, 0.1, 0.0, 0.0]),
              prev_a=np.array(1))
  
      def test_ppo_discr(self):
          do_test_explorations(
              ppo.PPOTrainer,
              "CartPole-v0",
              ppo.DEFAULT_CONFIG,
              np.array([0.0, 0.1, 0.0, 0.0]),
              prev_a=np.array(0))
  
      def test_ppo_cont(self):
          do_test_explorations(
              ppo.PPOTrainer,
              "Pendulum-v0",
              ppo.DEFAULT_CONFIG,
              np.array([0.0, 0.1, 0.0]),
              prev_a=np.array([0.0]),
              expected_mean_action=0.0)
  
      def test_sac(self):
          do_test_explorations(
              sac.SACTrainer,
              "Pendulum-v0",
              sac.DEFAULT_CONFIG,
              np.array([0.0, 0.1, 0.0]),
              expected_mean_action=0.0)
  
      def test_td3(self):
          config = td3.TD3_DEFAULT_CONFIG.copy()
          # Switch off random timesteps at beginning. We want to test actual
          # GaussianNoise right away.
          config["exploration_config"]["random_timesteps"] = 0
          do_test_explorations(
              td3.TD3Trainer,
              "Pendulum-v0",
              config,
              np.array([0.0, 0.1, 0.0]),
              expected_mean_action=0.0)
  
  
  if __name__ == "__main__":
      import pytest
      sys.exit(pytest.main(["-v", __file__]))
  