class_docstr: A buffer for storing trajectories experienced by a TRPO agent interacting
  with the environment, and using Generalized Advantage Estimation (GAE-lambda)
  for calculating the advantages of state-action pairs.
class_docstr_tok: ["A","buffer","for","storing","trajectories","experienced","by","a","TRPO","agent","interacting","with","the","environment",",","and","using","Generalized","Advantage","Estimation","(","GAE-lambda",")","for","calculating","the","advantages","of","state-action","pairs","."]
docstr: Append one timestep of agent-environment interaction to the buffer.
docstr_tok: ["Append","one","timestep","of","agent-environment","interaction","to","the","buffer","."]
code: def store(self, obs, act, rew, val, logp, mean, log_std):
      """
      Append one timestep of agent-environment interaction to the buffer.
      """
      assert (
          self.ptr < self.max_size
      )  # buffer has to have room so you can store
      self.obs_buf[self.ptr] = obs
      self.act_buf[self.ptr] = act
      self.rew_buf[self.ptr] = rew
      self.val_buf[self.ptr] = val
      self.logp_buf[self.ptr] = logp
      self.mean_buf[self.ptr] = mean
      self.log_std_buf[self.ptr] = log_std
      self.ptr += 1
  
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 2 for calling the GAE_Buffer obj's underlying member method code with example usage: obj.store(obs, act, rew, val, logp, mean, log_std) without return types