-
Notifications
You must be signed in to change notification settings - Fork 0
/
replay_buffer_policy.py
192 lines (161 loc) · 6.42 KB
/
replay_buffer_policy.py
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
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
import numpy as np
import random
from segment_tree import SumSegmentTree, MinSegmentTree
class ReplayBuffer(object):
def __init__(self, size):
"""Create Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. When the buffer
overflows the old memories are dropped.
"""
self._storage = []
self._maxsize = size
self._next_idx = 0
def __len__(self):
return len(self._storage)
def add(self, obs_t, action, policy, reward, obs_tp1, done):
data = (obs_t, action, policy, reward, obs_tp1, done)
if self._next_idx >= len(self._storage):
self._storage.append(data)
else:
self._storage[self._next_idx] = data
self._next_idx = (self._next_idx + 1) % self._maxsize
def _encode_sample(self, idxes):
obses_t, actions, policies, rewards, obses_tp1, dones = [], [], [], [], [], []
for i in idxes:
data = self._storage[i]
obs_t, action, policy, reward, obs_tp1, done = data
obses_t.append(np.array(obs_t, copy=False))
actions.append(np.array(action, copy=False))
policies.append(np.array(policy, copy=False))
rewards.append(reward)
obses_tp1.append(np.array(obs_tp1, copy=False))
dones.append(done)
return np.array(obses_t), np.array(actions), np.array(policies), np.array(rewards), np.array(obses_tp1), np.array(dones)
def sample(self, batch_size):
"""Sample a batch of experiences.
Parameters
----------
batch_size: int
How many transitions to sample.
Returns
-------
obs_batch: np.array
batch of observations
act_batch: np.array
batch of actions executed given obs_batch
rew_batch: np.array
rewards received as results of executing act_batch
next_obs_batch: np.array
next set of observations seen after executing act_batch
done_mask: np.array
done_mask[i] = 1 if executing act_batch[i] resulted in
the end of an episode and 0 otherwise.
"""
idxes = [random.randint(0, len(self._storage) - 1) for _ in range(batch_size)]
return self._encode_sample(idxes)
class PrioritizedReplayBuffer(ReplayBuffer):
def __init__(self, size, alpha):
"""Create Prioritized Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. When the buffer
overflows the old memories are dropped.
alpha: float
how much prioritization is used
(0 - no prioritization, 1 - full prioritization)
See Also
--------
ReplayBuffer.__init__
"""
super(PrioritizedReplayBuffer, self).__init__(size)
assert alpha >= 0
self._alpha = alpha
it_capacity = 1
while it_capacity < size:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
def add(self, *args, **kwargs):
"""See ReplayBuffer.store_effect"""
idx = self._next_idx
super().add(*args, **kwargs)
self._it_sum[idx] = self._max_priority ** self._alpha
self._it_min[idx] = self._max_priority ** self._alpha
def _sample_proportional(self, batch_size):
res = []
p_total = self._it_sum.sum(0, len(self._storage) - 1)
every_range_len = p_total / batch_size
for i in range(batch_size):
mass = random.random() * every_range_len + i * every_range_len
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
def sample(self, batch_size, beta):
"""Sample a batch of experiences.
compared to ReplayBuffer.sample
it also returns importance weights and idxes
of sampled experiences.
Parameters
----------
batch_size: int
How many transitions to sample.
beta: float
To what degree to use importance weights
(0 - no corrections, 1 - full correction)
Returns
-------
obs_batch: np.array
batch of observations
act_batch: np.array
batch of actions executed given obs_batch
rew_batch: np.array
rewards received as results of executing act_batch
next_obs_batch: np.array
next set of observations seen after executing act_batch
done_mask: np.array
done_mask[i] = 1 if executing act_batch[i] resulted in
the end of an episode and 0 otherwise.
weights: np.array
Array of shape (batch_size,) and dtype np.float32
denoting importance weight of each sampled transition
idxes: np.array
Array of shape (batch_size,) and dtype np.int32
idexes in buffer of sampled experiences
"""
assert beta > 0
idxes = self._sample_proportional(batch_size)
weights = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage)) ** (-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self._storage)) ** (-beta)
weights.append(weight / max_weight)
weights = np.array(weights)
encoded_sample = self._encode_sample(idxes)
return tuple(list(encoded_sample) + [weights, idxes])
def update_priorities(self, idxes, priorities):
"""Update priorities of sampled transitions.
sets priority of transition at index idxes[i] in buffer
to priorities[i].
Parameters
----------
idxes: [int]
List of idxes of sampled transitions
priorities: [float]
List of updated priorities corresponding to
transitions at the sampled idxes denoted by
variable `idxes`.
"""
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
assert priority > 0
assert 0 <= idx < len(self._storage)
self._it_sum[idx] = priority ** self._alpha
self._it_min[idx] = priority ** self._alpha
self._max_priority = max(self._max_priority, priority)