class_docstr: Experience Replay Buffer
  
  As described in
  https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf.
  
  Args:
      capacity (int): capacity in terms of number of transitions
      num_steps (int): Number of timesteps per stored transition
          (for N-step updates)
class_docstr_tok: ["Experience","Replay","Buffer","As","described","in","https",":","\/\/storage.googleapis.com\/deepmind-media\/dqn\/DQNNaturePaper.pdf",".","Args",":","capacity","(","int",")",":","capacity","in","terms","of","number","of","transitions","num_steps","(","int",")",":","Number","of","timesteps","per","stored","transition","(","for","N-step","updates",")"]
code: def __init__(self, capacity=None, num_steps=1):
      self.capacity = capacity
      assert num_steps > 0
      self.num_steps = num_steps
      self.memory = RandomAccessQueue(maxlen=capacity)
      self.last_n_transitions = collections.defaultdict(
          lambda: collections.deque([], maxlen=num_steps)
      )
  
file_path: chainerrl/chainerrl/replay_buffers/replay_buffer.py
imports: import collections
  import pickle
  from chainerrl.misc.collections import RandomAccessQueue
  from chainerrl import replay_buffer
label_desc: Use image node_id 1 to create a new ReplayBuffer object from inherited base classes: replay_buffer with example: obj = ReplayBuffer(capacity, num_steps)