class_docstr: A buffer for storing trajectories experienced by a VPG 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):
      """
      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.ptr += 1
  
file_path: spinningup/spinup/algos/tf1/vpg/vpg.py
imports: import numpy
  import tensorflow
  import gym
  import time
  import spinup.algos.tf1.vpg.core
  from spinup.utils.logx import EpochLogger
  from spinup.utils.mpi_tf import MpiAdamOptimizer, sync_all_params
  from spinup.utils.mpi_tools import mpi_fork, mpi_avg, proc_id, mpi_statistics_scalar, num_procs
label_desc: Use image node_id 2 for calling the VPGBuffer obj's underlying member method code with example usage: obj.store(obs, act, rew, val, logp) without return types