/
dqn.py
155 lines (123 loc) · 5.07 KB
/
dqn.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
import numpy as np
import tensorflow as tf
from ..agent import Agent
from ..registry import register
from .utils import copy_variables_op
from ...models.registry import get_model
from ...utils.logger import log_scalar, log_histogram
@register
class DQN(Agent):
""" Deep Q Network """
def __init__(self, sess, hparams):
super().__init__(sess, hparams)
# list of epsilon for each thread
hparams.epsilon = [hparams.max_epsilon] * hparams.num_workers
# set minimum epsilon for each worker
# https://arxiv.org/pdf/1602.01783.pdf Section 8
self._hparams.min_epsilon = list(
np.random.choice([0.1, 0.01, 0.5],
size=self._hparams.num_workers,
p=[0.4, 0.3, 0.3]))
self.model = get_model(hparams, register="basic", name="q_values")
self.target_model = get_model(
hparams, register="basic", name="target_q_values")
self.build()
def _epsilon_decay(self, worker_id=0):
if self._hparams.epsilon[worker_id] > self._hparams.min_epsilon[worker_id]:
self._hparams.epsilon[worker_id] *= self._hparams.epsilon_decay_rate
else:
self._hparams.epsilon[worker_id] = self._hparams.min_epsilon[worker_id]
log_scalar("epsilon/worker_%d" % worker_id,
self._hparams.epsilon[worker_id])
def act(self, state, worker_id=0):
if state.ndim < len(self._hparams.state_shape) + 1:
state = np.expand_dims(state, axis=0)
action_distribution = self._sess.run(
self.logits, feed_dict={self.last_states: state})
return self._action_function(self._hparams, action_distribution, worker_id)
def observe(self, last_state, action, reward, done, state, worker_id=0):
if done:
state = np.zeros(state.shape)
self._memory[worker_id].add_sample(
last_state=last_state,
action=action,
reward=reward,
discount=self._hparams.gamma,
done=done,
state=state,
)
if self._hparams.local_step[
worker_id] % self._hparams.batch_size == 0 or done:
self.update(worker_id)
if self._hparams.global_step % self._hparams.update_target_interval == 0:
self.update_targets()
def clone_weights(self):
self.target_model.set_weights(self.model.get_weights())
def update_targets(self):
self._sess.run(self.target_update_op)
def _build_target_update_op(self):
with tf.variable_scope("update_target_networks"):
self.target_update_op = copy_variables_op(
source=self.model, target=self.target_model)
def build(self):
self.last_states = tf.placeholder(
tf.float32, [None] + self._hparams.state_shape, name="last_states")
self.rewards = tf.placeholder(tf.float32, [None], name="rewards")
self.actions = tf.placeholder(tf.int32, [None], name="actions")
self.done = tf.placeholder(tf.bool, [None], name="done")
self.states = tf.placeholder(tf.float32, [None] + self._hparams.state_shape,
"states")
self.importance_sampling_weights = tf.placeholder(
tf.float32, [None], name="importance_sampling_weights")
last_states = self.process_states(self.last_states)
states = self.process_states(self.states)
if self._hparams.pixel_input:
self.cnn_vars = self._state_processor.trainable_weights
else:
self.cnn_vars = None
# predict q value Q(s, a)
self.logits = self.model(last_states)
# convert action to one hot vector
action_mask = tf.one_hot(self.actions, self._hparams.num_actions, axis=-1)
predict_q = tf.boolean_mask(self.logits, action_mask)
# target q value Q(s', a')
target_q = tf.where(
self.done, self.rewards, self.rewards +
self._hparams.gamma * tf.reduce_max(self.target_model(states), axis=-1))
# temporal difference
self.td_error = tf.abs(target_q - predict_q)
self.loss, self.train_op, self.state_processor_train_op = self._grad_function(
preds=predict_q,
targets=target_q,
hparams=self._hparams,
weights=self.importance_sampling_weights,
var_list={
'agent_vars': self.model.trainable_weights,
'cnn_vars': self.cnn_vars
})
self._build_target_update_op()
def update(self, worker_id=0):
if self._hparams.test_only:
return
memory = self._memory[worker_id]
if memory.size() > self._hparams.batch_size:
batch = memory.sample(self._hparams.batch_size)
loss, _, td_errors, _ = self._sess.run(
[
self.loss, self.train_op, self.td_error,
self.state_processor_train_op
],
feed_dict={
self.last_states: batch.last_state,
self.actions: batch.action,
self.rewards: batch.reward,
self.done: batch.done,
self.states: batch.state,
self.importance_sampling_weights: batch.weight
})
if self._hparams.memory == "prioritized":
memory.update(batch.index, td_errors)
if self._hparams.num_workers > 1:
memory.clear()
self._epsilon_decay(worker_id)
log_scalar("loss/worker_%d" % worker_id, loss)