132 lines
4.5 KiB
Python
132 lines
4.5 KiB
Python
import logging
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import numpy as np
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from .replay_buffer_base import ReplayBufferBase
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__all__ = [
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'VanillaReplayBuffer',
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]
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STATE = 0
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ACTION = 1
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REWARD = 2
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DONE = 3
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# TODO: valid data points could be less than `nstep` timesteps. Check priority replay paper!
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class VanillaReplayBuffer(ReplayBufferBase):
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"""
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Class for vanilla replay buffer as used in (Mnih, et al., 2015).
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Frames are always continuous in temporal order. They are only removed from the beginning
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and added at the tail.
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This continuity in `self.data` could be exploited only in vanilla replay buffer.
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:param capacity: An int. The capacity of the buffer.
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:param nstep: An int defaulting to 1. The number of timesteps to lookahead for temporal difference computation.
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Only continuous data pieces longer than this number or already terminated ones are
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considered valid data points.
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"""
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def __init__(self, capacity, nstep=1):
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assert capacity > 0
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self.capacity = int(capacity)
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self.nstep = nstep
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self.data = [[]]
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self.index = [[]]
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self.candidate_index = 0
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self.size = 0 # number of valid data points (not frames)
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self.index_lengths = [0] # for sampling
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def add(self, frame):
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"""
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Adds one frame of data to the buffer.
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:param frame: A tuple of (observation, action, reward, done_flag).
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"""
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self.data[-1].append(frame)
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has_enough_frames = len(self.data[-1]) > self.nstep
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if frame[DONE]: # episode terminates, all trailing frames become valid data points
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trailing_index = list(range(self.candidate_index, len(self.data[-1])))
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self.index[-1] += trailing_index
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self.size += len(trailing_index)
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self.index_lengths[-1] += len(trailing_index)
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# prepare for the next episode
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self.data.append([])
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self.index.append([])
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self.candidate_index = 0
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self.index_lengths.append(0)
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elif has_enough_frames: # add one valid data point
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self.index[-1].append(self.candidate_index)
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self.candidate_index += 1
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self.size += 1
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self.index_lengths[-1] += 1
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# automated removal to capacity
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if self.size > self.capacity:
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self.remove()
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def remove(self):
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"""
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Removes data from the buffer until ``self.size <= self.capacity``.
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"""
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if self.size:
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while self.size > self.capacity:
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self._remove_oldest()
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else:
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logging.warning('Attempting to remove from empty buffer!')
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def _remove_oldest(self):
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"""
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Removes the oldest data point, in this case, just the oldest frame. Empty episodes are also removed
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if resulted from removal.
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"""
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self.index[0].pop() # note that all index of frames in the first episode are shifted forward by 1
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if self.index[0]: # first episode still has data points
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self.data[0].pop(0)
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if len(self.data) == 1: # otherwise self.candidate index is for another episode
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self.candidate_index -= 1
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self.index_lengths[0] -= 1
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else: # first episode becomes empty
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self.data.pop(0)
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self.index.pop(0)
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if len(self.data) == 0: # otherwise self.candidate index is for another episode
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self.candidate_index = 0
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self.index_lengths.pop(0)
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self.size -= 1
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def sample(self, batch_size):
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"""
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Performs uniform random sampling on ``self.index``. For simplicity, we do random sampling with replacement
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for now with time O(``batch_size``). Fastest sampling without replacement seems to have to be of time
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O(``batch_size`` * log(num_episodes)).
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:param batch_size: An int. The size of the minibatch.
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:return: A list of list of the sampled indexes. Episodes without sampled data points
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correspond to empty sub-lists.
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"""
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prob_episode = np.array(self.index_lengths) * 1. / self.size
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num_episodes = len(self.index)
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sampled_index = [[] for _ in range(num_episodes)]
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for _ in range(batch_size):
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# sample which episode
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sampled_episode_i = int(np.random.choice(num_episodes, p=prob_episode))
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# sample which data point within the sampled episode
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sampled_frame_i = int(np.random.randint(self.index_lengths[sampled_episode_i]))
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sampled_index[sampled_episode_i].append(sampled_frame_i)
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return sampled_index
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