docstr: update policy model self.model with policy gradient algorithm
          
code: def learn(self, obs, action, reward):
      """update policy model self.model with policy gradient algorithm"""
      act_prob = self.model(obs)
      log_prob = layers.cross_entropy(act_prob, action)
      cost = log_prob * reward
      cost = layers.reduce_mean(cost)
      optimizer = fluid.optimizer.Adam(self.lr)
      optimizer.minimize(cost)
      return cost
  
file_path: PARL/parl/algorithms/fluid/policy_gradient.py
imports: import warnings
  import paddle.fluid
  from parl.core.fluid.algorithm import Algorithm
  from parl.core.fluid import layers
label_desc: Use image node_id 3 for calling the PolicyGradient obj's underlying member method code with example usage: obj.learn(obs, action, reward) and returns: cost