More realistic value_weight

  env = TicTacToeEnvironment()

  policy = TicTacToePolicy()

  a3c = dc.rl.A3C(

      env, policy, entropy_weight=0, value_weight=0.0001)

      env, policy, entropy_weight=0, value_weight=0.25)

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

      tf.train.AdamOptimizer, learning_rate=0.01)

  a3c.fit(10000)

  a3c.fit(100000)

  env.reset()

  while not env._terminated:

    print(env.display())