-
Notifications
You must be signed in to change notification settings - Fork 1
/
Random_Network_Distillation.py
164 lines (133 loc) · 6.13 KB
/
Random_Network_Distillation.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
"""This is a simple implementation of [Exploration by Random Network Distillation](https://arxiv.org/abs/1810.12894)"""
import numpy as np
import tensorflow as tf
import gym
import matplotlib.pyplot as plt
class CuriosityNet:
def __init__(
self,
n_a,
n_s,
lr=0.01,
gamma=0.95,
epsilon=1.,
replace_target_iter=300,
memory_size=10000,
batch_size=128,
output_graph=False,
):
self.n_a = n_a
self.n_s = n_s
self.lr = lr
self.gamma = gamma
self.epsilon = epsilon
self.replace_target_iter = replace_target_iter
self.memory_size = memory_size
self.batch_size = batch_size
self.s_encode_size = 1000 # give a hard job for predictor to learn
# total learning step
self.learn_step_counter = 0
self.memory_counter = 0
# initialize zero memory [s, a, r, s_]
self.memory = np.zeros((self.memory_size, n_s * 2 + 2))
self.tfs, self.tfa, self.tfr, self.tfs_, self.pred_train, self.dqn_train, self.q = \
self._build_nets()
t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_net')
e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='eval_net')
with tf.variable_scope('hard_replacement'):
self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
self.sess = tf.Session()
if output_graph:
tf.summary.FileWriter("logs/", self.sess.graph)
self.sess.run(tf.global_variables_initializer())
def _build_nets(self):
tfs = tf.placeholder(tf.float32, [None, self.n_s], name="s") # input State
tfa = tf.placeholder(tf.int32, [None, ], name="a") # input Action
tfr = tf.placeholder(tf.float32, [None, ], name="ext_r") # extrinsic reward
tfs_ = tf.placeholder(tf.float32, [None, self.n_s], name="s_") # input Next State
# fixed random net
with tf.variable_scope("random_net"):
rand_encode_s_ = tf.layers.dense(tfs_, self.s_encode_size)
# predictor
ri, pred_train = self._build_predictor(tfs_, rand_encode_s_)
# normal RL model
q, dqn_loss, dqn_train = self._build_dqn(tfs, tfa, ri, tfr, tfs_)
return tfs, tfa, tfr, tfs_, pred_train, dqn_train, q
def _build_predictor(self, s_, rand_encode_s_):
with tf.variable_scope("predictor"):
net = tf.layers.dense(s_, 128, tf.nn.relu)
out = tf.layers.dense(net, self.s_encode_size)
with tf.name_scope("int_r"):
ri = tf.reduce_sum(tf.square(rand_encode_s_ - out), axis=1) # intrinsic reward
train_op = tf.train.RMSPropOptimizer(self.lr, name="predictor_opt").minimize(
tf.reduce_mean(ri), var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "predictor"))
return ri, train_op
def _build_dqn(self, s, a, ri, re, s_):
with tf.variable_scope('eval_net'):
e1 = tf.layers.dense(s, 128, tf.nn.relu)
q = tf.layers.dense(e1, self.n_a, name="q")
with tf.variable_scope('target_net'):
t1 = tf.layers.dense(s_, 128, tf.nn.relu)
q_ = tf.layers.dense(t1, self.n_a, name="q_")
with tf.variable_scope('q_target'):
q_target = re + ri + self.gamma * tf.reduce_max(q_, axis=1, name="Qmax_s_")
with tf.variable_scope('q_wrt_a'):
a_indices = tf.stack([tf.range(tf.shape(a)[0], dtype=tf.int32), a], axis=1)
q_wrt_a = tf.gather_nd(params=q, indices=a_indices)
loss = tf.losses.mean_squared_error(labels=q_target, predictions=q_wrt_a) # TD error
train_op = tf.train.RMSPropOptimizer(self.lr, name="dqn_opt").minimize(
loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "eval_net"))
return q, loss, train_op
def store_transition(self, s, a, r, s_):
transition = np.hstack((s, [a, r], s_))
# replace the old memory with new memory
index = self.memory_counter % self.memory_size
self.memory[index, :] = transition
self.memory_counter += 1
def choose_action(self, observation):
# to have batch dimension when feed into tf placeholder
s = observation[np.newaxis, :]
if np.random.uniform() < self.epsilon:
# forward feed the observation and get q value for every actions
actions_value = self.sess.run(self.q, feed_dict={self.tfs: s})
action = np.argmax(actions_value)
else:
action = np.random.randint(0, self.n_a)
return action
def learn(self):
# check to replace target parameters
if self.learn_step_counter % self.replace_target_iter == 0:
self.sess.run(self.target_replace_op)
# sample batch memory from all memory
top = self.memory_size if self.memory_counter > self.memory_size else self.memory_counter
sample_index = np.random.choice(top, size=self.batch_size)
batch_memory = self.memory[sample_index, :]
bs, ba, br, bs_ = batch_memory[:, :self.n_s], batch_memory[:, self.n_s], \
batch_memory[:, self.n_s + 1], batch_memory[:, -self.n_s:]
self.sess.run(self.dqn_train, feed_dict={self.tfs: bs, self.tfa: ba, self.tfr: br, self.tfs_: bs_})
if self.learn_step_counter % 100 == 0: # delay training in order to stay curious
self.sess.run(self.pred_train, feed_dict={self.tfs_: bs_})
self.learn_step_counter += 1
env = gym.make('MountainCar-v0')
env = env.unwrapped
dqn = CuriosityNet(n_a=3, n_s=2, lr=0.01, output_graph=False)
ep_steps = []
for epi in range(200):
s = env.reset()
steps = 0
while True:
# env.render()
a = dqn.choose_action(s)
s_, r, done, info = env.step(a)
dqn.store_transition(s, a, r, s_)
dqn.learn()
if done:
print('Epi: ', epi, "| steps: ", steps)
ep_steps.append(steps)
break
s = s_
steps += 1
plt.plot(ep_steps)
plt.ylabel("steps")
plt.xlabel("episode")
plt.show()