docstr: Test A2C on an environment with a continous action space.
code: def test_continuous(self):
      """Test A2C on an environment with a continous action space."""
  
      # The state consists of two numbers: a current value and a target value.
      # The policy just needs to learn to output the target value (or at least
      # move toward it).
  
      class TestEnvironment(dc.rl.Environment):
          def __init__(self):
              super(TestEnvironment, self).__init__(
                  (2,), action_shape=(1,)
              )
  
          def reset(self):
              target = np.random.uniform(-50, 50)
              self._state = np.array([0, target], dtype=np.float32)
              self._terminated = False
              self.count = 0
  
          def step(self, action):
              target = self._state[1]
              dist = np.abs(target - action[0])
              old_dist = np.abs(target - self._state[0])
              new_state = np.array(
                  [action[0], target], dtype=np.float32
              )
              self._state = new_state
              self.count += 1
              reward = old_dist - dist
              self._terminated = self.count == 10
              return reward
  
      # A simple policy with no hidden layers.
  
      class TestPolicy(dc.rl.Policy):
          def __init__(self):
              super(TestPolicy, self).__init__(
                  ["action_mean", "action_std", "value"]
              )
  
          def create_model(self, **kwargs):
              class TestModel(tf.keras.Model):
                  def __init__(self):
                      super(TestModel, self).__init__(**kwargs)
                      self.mean = Dense(1, kernel_initializer="zeros")
                      self.std = tf.constant([10.0])
                      self.value = Dense(1)
  
                  def call(self, inputs, **kwargs):
                      return (
                          self.mean(inputs[0]),
                          self.std,
                          self.value(inputs[0]),
                      )
  
              return TestModel()
  
      # Optimize it.
  
      env = TestEnvironment()
      learning_rate = PolynomialDecay(
          initial_rate=0.005, final_rate=0.0005, decay_steps=25000
      )
      a2c = dc.rl.A2C(
          env,
          TestPolicy(),
          discount_factor=0,
          optimizer=Adam(learning_rate=learning_rate),
      )
      a2c.fit(25000)
  
      # Try running it and see if it reaches the target
  
      env.reset()
      while not env.terminated:
          env.step(a2c.select_action(env.state, deterministic=True))
      distance = np.abs(env.state[0] - env.state[1])
      tolerance = max(1.0, 0.1 * np.abs(env.state[1]))
      assert distance < tolerance
  
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 4 for calling the TestA2C obj's underlying member method code with example usage: obj.test_continuous() and returns: reward, TestModel, self,  self,  self