class_docstr: Replay Memory for saving data.
  Args:
      max_size (int): size of replay memory
      obs_dim (int): dimension of the observation
class_docstr_tok: ["Replay","Memory","for","saving","data",".","Args",":","max_size","(","int",")",":","size","of","replay","memory","obs_dim","(","int",")",":","dimension","of","the","observation"]
code: def reset(self):
      self.obs = np.zeros(
          (self.max_size, self.obs_dim), dtype="float32"
      )
      self.action = np.zeros((self.max_size,), dtype="int32")
      self.value = np.zeros((self.max_size,), dtype="float32")
      self.returns = np.zeros((self.max_size,), dtype="float32")
      self.log_prob = np.zeros((self.max_size,), dtype="float32")
      self.adv = np.zeros((self.max_size,), dtype="float32")
  
      self._curr_size = 0
      self._curr_pos = 0
  
file_path: PARL/examples/Baselines/Halite_competition/torch/rl_trainer/replay_memory.py
imports: import numpy
label_desc: Use image node_id 8 for calling the ReplayMemory obj's underlying member method code with example usage: obj.reset() without return types