File size: 4,429 Bytes
a54010a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
import random
from collections import deque
import itertools
import numpy as np
class ReplayBuffer(object):
def __init__(self, buffer_size, random_seed=123):
"""
The right side of the deque contains the most recent experiences
"""
self.buffer_size = buffer_size
self.count = 0
self.buffer = deque()
random.seed(random_seed)
def add(self, s, a, r, t, s2):
experience = (s, a, r, t, s2)
if self.count < self.buffer_size:
self.buffer.append(experience)
self.count += 1
else:
self.buffer.popleft()
self.buffer.append(experience)
def size(self):
return self.count
def sample_batch(self, batch_size):
if self.count < batch_size:
batch = random.sample(self.buffer, self.count)
else:
batch = random.sample(self.buffer, batch_size)
s_batch = np.array([_[0] for _ in batch])
a_batch = np.array([_[1] for _ in batch])
r_batch = np.array([_[2] for _ in batch]).reshape(-1, 1)
t_batch = np.array([_[3] for _ in batch]).reshape(-1, 1)
s2_batch = np.array([_[4] for _ in batch])
return s_batch, a_batch, r_batch, t_batch, s2_batch
def return_buffer(self):
s = np.array([_[0] for _ in self.buffer])
a = np.array([_[1] for _ in self.buffer])
r = np.array([_[2] for _ in self.buffer]).reshape(-1, 1)
t = np.array([_[3] for _ in self.buffer]).reshape(-1, 1)
s2 = np.array([_[4] for _ in self.buffer])
return s, a, r, t, s2
def clear(self):
self.buffer.clear()
self.count = 0
class RolloutReplayBuffer(object):
def __init__(self, buffer_size, random_seed=123, history_len=10):
"""
The right side of the deque contains the most recent experiences
"""
self.buffer_size = buffer_size
self.count = 0
self.buffer = deque(maxlen=buffer_size)
random.seed(random_seed)
self.buffer.append([])
self.history_len = history_len
def add(self, s, a, r, t, s2):
experience = (s, a, r, t, s2)
if t:
self.count += 1
self.buffer[-1].append(experience)
self.buffer.append([])
else:
self.buffer[-1].append(experience)
def size(self):
return self.count
def sample_batch(self, batch_size):
if self.count < batch_size:
batch = random.sample(
list(itertools.islice(self.buffer, 0, len(self.buffer) - 1)), self.count
)
else:
batch = random.sample(
list(itertools.islice(self.buffer, 0, len(self.buffer) - 1)), batch_size
)
idx = [random.randint(0, len(b) - 1) for b in batch]
s_batch = []
s2_batch = []
for i in range(len(batch)):
if idx[i] == len(batch[i]):
s = batch[i]
s2 = batch[i]
else:
s = batch[i][: idx[i] + 1]
s2 = batch[i][: idx[i] + 1]
s = [v[0] for v in s]
s = s[::-1]
s2 = [v[4] for v in s2]
s2 = s2[::-1]
if len(s) < self.history_len:
missing = self.history_len - len(s)
s += [s[-1]] * missing
s2 += [s2[-1]] * missing
else:
s = s[: self.history_len]
s2 = s2[: self.history_len]
s = s[::-1]
s_batch.append(s)
s2 = s2[::-1]
s2_batch.append(s2)
a_batch = np.array([batch[i][idx[i]][1] for i in range(len(batch))])
r_batch = np.array([batch[i][idx[i]][2] for i in range(len(batch))]).reshape(
-1, 1
)
t_batch = np.array([batch[i][idx[i]][3] for i in range(len(batch))]).reshape(
-1, 1
)
return np.array(s_batch), a_batch, r_batch, t_batch, np.array(s2_batch)
def return_buffer(self):
s = np.array([_[0] for _ in self.buffer])
a = np.array([_[1] for _ in self.buffer])
r = np.array([_[2] for _ in self.buffer]).reshape(-1, 1)
t = np.array([_[3] for _ in self.buffer]).reshape(-1, 1)
s2 = np.array([_[4] for _ in self.buffer])
return s, a, r, t, s2
def clear(self):
self.buffer.clear()
self.count = 0
|