Can play tic tac toe

    if self.game_over():

      self._terminated = True

      print("Draw")

      return TicTacToeEnvironment.DRAW_REWARD

    move = self.get_O_move()

class TicTacToePolicy(dc.rl.Policy):

  def create_layers(self, state, **kwargs):

    d1 = Conv2D(num_outputs=64, kernel_size=3, in_layers=state)

    d1 = Flatten(in_layers=[d1])

    d1 = Flatten(in_layers=state)

    d2 = Dense(

        in_layers=[d1],

        activation_fn=tf.nn.relu,

  timeout = 60 * 60  # One Hour

  while end - start < timeout:

    a3c = dc.rl.A3C(

        env, policy, entropy_weight=0, model_dir="/home/leswing/tictactoe")

        env, policy, entropy_weight=0, value_weight=0.0001, model_dir="/home/leswing/tictactoe")

    a3c.optimizer = dc.models.tensorgraph.TFWrapper(

        tf.train.AdamOptimizer, learning_rate=0.01)

    TicTacToeEnvironment.ILLEGAL_MOVE_PENALTY = -10000.0

    try:

      a3c.restore()

    except:

        advantages * tf.reduce_sum(action * log_prob))

    value_loss = tf.reduce_sum(tf.square(reward - value))

    entropy = -tf.reduce_sum(prob * log_prob)

    entropy = tf.Print(entropy, [entropy])

    self.out_tensor = policy_loss + self.value_weight * value_loss - self.entropy_weight * entropy

    return self.out_tensor