docstr: Test training a policy for the roulette environment.
code: def test_roulette(self):
      """Test training a policy for the roulette environment."""
  
      # This is modeled after the Roulette-v0 environment from OpenAI Gym.
      # The player can bet on any number from 0 to 36, or walk away (which ends the
      # game).  The average reward for any bet is slightly negative, so the best
      # strategy is to walk away.
  
      class RouletteEnvironment(dc.rl.Environment):
          def __init__(self):
              super(RouletteEnvironment, self).__init__([(1,)], 38)
              self._state = [np.array([0])]
  
          def step(self, action):
              if action == 37:
                  self._terminated = True  # Walk away.
                  return 0.0
              wheel = np.random.randint(37)
              if wheel == 0:
                  if action == 0:
                      return 35.0
                  return -1.0
              if action != 0 and wheel % 2 == action % 2:
                  return 1.0
              return -1.0
  
          def reset(self):
              self._terminated = False
  
      env = RouletteEnvironment()
  
      # This policy just learns a constant probability for each action, and a constant for the value.
  
      class TestPolicy(dc.rl.Policy):
          def __init__(self):
              super(TestPolicy, self).__init__(["action_prob", "value"])
  
          def create_model(self, **kwargs):
              class TestModel(tf.keras.Model):
                  def __init__(self):
                      super(TestModel, self).__init__(**kwargs)
                      self.action = tf.Variable(
                          np.ones(env.n_actions, np.float32)
                      )
                      self.value = tf.Variable([0.0], tf.float32)
  
                  def call(self, inputs, **kwargs):
                      prob = tf.nn.softmax(
                          tf.reshape(self.action, (-1, env.n_actions))
                      )
                      return (prob, self.value)
  
              return TestModel()
  
      # Optimize it.
  
      a2c = dc.rl.A2C(
          env,
          TestPolicy(),
          max_rollout_length=20,
          optimizer=Adam(learning_rate=0.001),
      )
      a2c.fit(100000)
  
      # It should have learned that the expected value is very close to zero, and that the best
      # action is to walk away.  (To keep the test fast, we allow that to be either of the two
      # top actions).
  
      action_prob, value = a2c.predict([[0]])
      assert -0.5 < value[0] < 0.5
      assert action_prob.argmax() == 37
      assert 37 in np.argsort(action_prob.flatten())[-2:]
      assert (
          a2c.select_action([[0]], deterministic=True)
          == action_prob.argmax()
      )
  
      # Verify that we can create a new A2C object, reload the parameters from the first one, and
      # get the same result.
  
      new_a2c = dc.rl.A2C(
          env, TestPolicy(), model_dir=a2c._model.model_dir
      )
      new_a2c.restore()
      action_prob2, value2 = new_a2c.predict([[0]])
      assert value2 == value
  
      # Do the same thing, only using the "restore" argument to fit().
  
      new_a2c = dc.rl.A2C(
          env, TestPolicy(), model_dir=a2c._model.model_dir
      )
      new_a2c.fit(0, restore=True)
      action_prob2, value2 = new_a2c.predict([[0]])
      assert value2 == value
  
file_path: deepchem/deepchem/rl/tests/test_a2c.py
imports: import unittest
  import pytest
  import numpy
  from flaky import flaky
  import deepchem
  from deepchem.models.optimizers import Adam, PolynomialDecay
label_desc: Use image node_id 1 for calling the TestA2C obj's underlying member method code with example usage: obj.test_roulette() and returns: unknown, TestModel, int, unknown, int, int, prob,  self