/
neural_net.py
212 lines (177 loc) · 8.91 KB
/
neural_net.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
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
import theano, sys, copy
import theano.tensor as T
import numpy as np
from collections import OrderedDict
from nnet import Model
from theano.ifelse import ifelse
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams
class MLP3D():
def __init__(self, num_options, model_network, temp=1):
self.temp = temp
self.options_W = theano.shared(np.random.uniform(
size=(num_options, model_network[-2]["out_size"], model_network[-1]["out_size"]), high=1, low=-1))
self.options_b = theano.shared(np.zeros((num_options, model_network[-1]["out_size"])))
self.params = [self.options_W, self.options_b]
def apply(self, inputs, option):
W = self.options_W[option]
b = self.options_b[option]
dots = T.sum(inputs.dimshuffle(0,1,'x')*W, axis=1)
return T.nnet.softmax((dots + b)/self.temp)
def save_params(self):
return [i.get_value() for i in self.params]
def load_params(self, values):
print "LOADING NNET..",
for p, value in zip(self.params, values):
p.set_value(value)
print "LOADED"
class OptionCritic_Network():
def __init__(self, model_network=None, gamma=0.99, learning_method="rmsprop", actor_lr=0.00025,
batch_size=32, input_size=None, learning_params=None, dnn_type=True, clip_delta=0,
scale=255., freeze_interval=100, grad_clip=0, termination_reg=0, num_options=8,
double_q=False, temp=1, entropy_reg=0, BASELINE=False, **kwargs):
x = T.ftensor4()
next_x = T.ftensor4()
a = T.ivector()
o = T.ivector()
r = T.fvector()
terminal = T.ivector()
self.freeze_interval = freeze_interval
self.theano_rng = MRG_RandomStreams(1000)
self.x_shared = theano.shared(np.zeros(tuple([batch_size]+input_size[1:]), dtype='float32'))
self.next_x_shared = theano.shared(np.zeros(tuple([batch_size]+input_size[1:]), dtype='float32'))
self.a_shared = theano.shared(np.zeros((batch_size), dtype='int32'))
self.o_shared = theano.shared(np.zeros((batch_size), dtype='int32'))
self.terminal_shared = theano.shared(np.zeros((batch_size), dtype='int32'))
self.r_shared = theano.shared(np.zeros((batch_size), dtype='float32'))
state_network = model_network[:-1]
termination_network = copy.deepcopy([model_network[-1]])
termination_network[0]["activation"] = "sigmoid"
print "NUM OPTIONS --->", num_options
termination_network[0]["out_size"] = num_options
option_network = copy.deepcopy([model_network[-1]])
option_network[0]["activation"] = "softmax"
Q_network = copy.deepcopy([model_network[-1]])
Q_network[0]["out_size"] = num_options
self.state_model = Model(state_network, input_size=input_size, dnn_type=dnn_type)
self.state_model_prime = Model(state_network, input_size=input_size, dnn_type=dnn_type)
output_size = [None,model_network[-2]["out_size"]]
self.Q_model = Model(Q_network, input_size=output_size, dnn_type=dnn_type)
self.Q_model_prime = Model(Q_network, input_size=output_size, dnn_type=dnn_type)
self.termination_model = Model(termination_network, input_size=output_size, dnn_type=dnn_type)
self.options_model = MLP3D(num_options, model_network, temp=temp)
s = self.state_model.apply(x/scale)
next_s = self.state_model.apply(next_x/scale)
next_s_prime = self.state_model_prime.apply(next_x/scale)
termination_probs = self.termination_model.apply(theano.gradient.disconnected_grad(s))
option_term_prob = termination_probs[T.arange(o.shape[0]), o]
next_termination_probs = self.termination_model.apply(theano.gradient.disconnected_grad(next_s))
next_option_term_prob = next_termination_probs[T.arange(o.shape[0]), o]
termination_sample = T.gt(option_term_prob, self.theano_rng.uniform(size=o.shape))
Q = self.Q_model.apply(s)
next_Q = self.Q_model.apply(next_s)
next_Q_prime = theano.gradient.disconnected_grad(self.Q_model_prime.apply(next_s_prime))
disc_option_term_prob = theano.gradient.disconnected_grad(next_option_term_prob)
action_probs = self.options_model.apply(s, o)
sampled_actions = T.argmax(self.theano_rng.multinomial(pvals=action_probs, n=1), axis=1).astype("int32")
if double_q:
print "TRAINING DOUBLE_Q"
y = r + (1-terminal)*gamma*(
(1-disc_option_term_prob)*next_Q_prime[T.arange(o.shape[0]), o] +
disc_option_term_prob*next_Q_prime[T.arange(next_Q.shape[0]), T.argmax(next_Q, axis=1)])
else:
y = r + (1-terminal)*gamma*(
(1-disc_option_term_prob)*next_Q_prime[T.arange(o.shape[0]), o] +
disc_option_term_prob*T.max(next_Q_prime, axis=1))
y = theano.gradient.disconnected_grad(y)
option_Q = Q[T.arange(o.shape[0]), o]
td_errors = y - option_Q
if clip_delta > 0:
quadratic_part = T.minimum(abs(td_errors), clip_delta)
linear_part = abs(td_errors) - quadratic_part
td_cost = 0.5 * quadratic_part ** 2 + clip_delta * linear_part
else:
td_cost = 0.5 * td_errors ** 2
#critic updates
critic_cost = T.sum(td_cost)
critic_params = self.Q_model.params + self.state_model.params
learning_algo = self.Q_model.get_learning_method(learning_method, **learning_params)
grads = T.grad(critic_cost, critic_params)
critic_updates = learning_algo.apply(critic_params, grads, grad_clip=grad_clip)
#actor updates
actor_params = self.termination_model.params + self.options_model.params
learning_algo = self.termination_model.get_learning_method("sgd", lr=actor_lr)
disc_Q = theano.gradient.disconnected_grad(option_Q)
disc_V = theano.gradient.disconnected_grad(T.max(Q, axis=1))
term_grad = T.sum(option_term_prob*(disc_Q-disc_V+termination_reg))
entropy = -T.sum(action_probs*T.log(action_probs))
if not BASELINE:
policy_grad = -T.sum(T.log(action_probs[T.arange(a.shape[0]), a]) * y) - entropy_reg*entropy
else:
policy_grad = -T.sum(T.log(action_probs[T.arange(a.shape[0]), a]) * (y-disc_Q)) - entropy_reg*entropy
grads = T.grad(term_grad+policy_grad, actor_params)
actor_updates = learning_algo.apply(actor_params, grads, grad_clip=grad_clip)
if self.freeze_interval > 1:
target_updates = OrderedDict()
for t, b in zip(self.Q_model_prime.params+self.state_model_prime.params,
self.Q_model.params+self.state_model.params):
target_updates[t] = b
self._update_target_params = theano.function([], [], updates=target_updates)
self.update_target_params()
print "freeze interval:", self.freeze_interval
else:
print "freeze interval: None"
critic_givens = {x:self.x_shared, o:self.o_shared, r:self.r_shared,
terminal:self.terminal_shared, next_x:self.next_x_shared}
actor_givens = {a:self.a_shared, r:self.r_shared,
terminal:self.terminal_shared, o:self.o_shared, next_x:self.next_x_shared}
print "compiling...",
self.train_critic = theano.function([], [critic_cost], updates=critic_updates, givens=critic_givens)
self.train_actor = theano.function([s], [], updates=actor_updates, givens=actor_givens)
self.pred_score = theano.function([], T.max(Q, axis=1), givens={x:self.x_shared})
self.sample_termination = theano.function([s], [termination_sample,T.argmax(Q, axis=1)], givens={o:self.o_shared})
self.sample_options = theano.function([s], T.argmax(Q, axis=1))
self.sample_actions = theano.function([s], sampled_actions, givens={o:self.o_shared})
self.get_action_dist = theano.function([s, o], action_probs)
self.get_s = theano.function([], s, givens={x:self.x_shared})
print "complete"
def update_target_params(self):
if self.freeze_interval > 1:
self._update_target_params()
return
def predict_move(self, s):
return self.sample_options(s)
def predict_termination(self, s, a):
self.a_shared.set_value(a)
return tuple(self.sample_termination(s))
def get_q_vals(self, x):
self.x_shared.set_value(x)
return self.pred_score()[:,np.newaxis]
def get_state(self, x):
self.x_shared.set_value(x)
return self.get_s()
def get_action(self, s, o):
self.o_shared.set_value(o)
return self.sample_actions(s)
def train_conv_net(self, train_set_x, next_x, options, r, terminal, actions=None, model=""):
self.next_x_shared.set_value(next_x)
self.o_shared.set_value(options)
self.r_shared.set_value(r)
self.terminal_shared.set_value(terminal)
if model == "critic":
self.x_shared.set_value(train_set_x)
return self.train_critic()
elif model == "actor":
self.a_shared.set_value(actions)
return self.train_actor(train_set_x)
else:
print "WRONG MODEL NAME"
raise NotImplementedError
def save_params(self):
return [self.state_model.save_params(), self.Q_model.save_params(), self.termination_model.save_params(),
self.options_model.save_params()]
def load_params(self, values):
self.state_model.load_params(values[0])
self.Q_model.load_params(values[1])
self.termination_model.load_params(values[2])
self.options_model.load_params(values[3])