class_docstr: trpo class
class_docstr_tok: ["trpo","class"]
docstr: train v function
  :param states: state batch
  :param rewards_to_go: rewards-to-go batch
  :return: None
docstr_tok: ["train","v","function",":","param","states",":","state","batch",":","param","rewards_to_go",":","rewards-to-go","batch",":","return",":","None"]
code: def train_vf(self, states, rewards_to_go):
      """
      train v function
      :param states: state batch
      :param rewards_to_go: rewards-to-go batch
      :return: None
      """
      with tf.GradientTape() as tape:
          value = self.critic(states)
          loss = tf.reduce_mean((rewards_to_go - value[:, 0]) ** 2)
      grad = tape.gradient(loss, self.critic.trainable_weights)
      self.critic_optimizer.apply_gradients(
          zip(grad, self.critic.trainable_weights)
      )
  
imports: import argparse
  import copy
  import os
  import threading
  import time
  import gym
  import matplotlib.pyplot
  import numpy
  import scipy.signal
  import tensorflow
  import tensorflow_probability
  import tensorlayer
label_desc: Use image node_id 5 for calling the TRPO obj's underlying member method code with example usage: obj.train_vf(states, rewards_to_go) without return types