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Model_actor_critic.py
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from __future__ import print_function
import paddle
import paddle.fluid as fluid
import numpy as np
import sys
def conv_bn_layer(main_input, ch_out, filter_size, stride, padding, act='relu', name=None):
conv = fluid.layers.conv2d(
input=main_input, # shape = [N,C,H,W]
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
use_cudnn=True,
use_mkldnn=False,
act=act,
name=name
)
return conv
class AC(object):
def __init__(self, A_DIM=4, gamma=0.94):
self.A_DIM = A_DIM
self.probs = None
self.prob_program = None
self.gamma = gamma
self.reward = None
self.td_error = None
self.a_p = fluid.Program()
self.c_p = fluid.Program()
self.startup = fluid.Program()
def act(self, state):
with fluid.unique_name.guard():
with fluid.program_guard(self.a_p, self.startup):
state = np.array(state, dtype='float32')
state = np.expand_dims(state, axis=0)
return self.exe.run(self.prob_program,
feed={'current_state': state},
fetch_list=[self.probs])[0]
def get_td_error(self, current_state, next_state, reward):
with fluid.unique_name.guard():
with fluid.program_guard(self.c_p, self.startup):
current_state = np.array(current_state, dtype='float32')
current_state = np.expand_dims(current_state, axis=0)
next_state = np.array(next_state, dtype='float32')
next_state = np.expand_dims(next_state, axis=0)
reward = np.expand_dims(np.array([reward], dtype='float32'), axis=0)
return self.exe.run(self.td_program,
feed={'current_state': current_state,
'next_state': next_state,
'reward': reward},
fetch_list=[self.c_target])[0]
def build_a(self, s):
a_conv1 = conv_bn_layer(main_input=s, ch_out=32, filter_size=3, stride=2, padding=1,name='a_conv1')
a_conv2 = conv_bn_layer(main_input=a_conv1, ch_out=32, filter_size=3, stride=2, padding=1,name='a_conv2')
a_conv3 = conv_bn_layer(main_input=a_conv2, ch_out=32, filter_size=3, stride=2, padding=1,name='a_conv3')
a_conv4 = conv_bn_layer(main_input=a_conv3, ch_out=32, filter_size=3, stride=2, padding=1,name='a_conv4')
a_fc5 = fluid.layers.fc(input=a_conv4, size=50, act='relu',name='a_fc5')
return fluid.layers.fc(input=a_fc5, size=self.A_DIM, act='softmax',name='a_out')
def build_c(self, s):
a_conv1 = conv_bn_layer(main_input=s, ch_out=32, filter_size=3, stride=2, padding=1,name='c_conv1')
a_conv2 = conv_bn_layer(main_input=a_conv1, ch_out=32, filter_size=3, stride=2, padding=1,name='c_conv2')
a_conv3 = conv_bn_layer(main_input=a_conv2, ch_out=32, filter_size=3, stride=2, padding=1,name='c_conv3')
a_conv4 = conv_bn_layer(main_input=a_conv3, ch_out=32, filter_size=3, stride=2, padding=1,name='c_conv4')
a_fc5 = fluid.layers.fc(input=a_conv4, size=256, act='relu',name='c_fc5')
value = fluid.layers.fc(input=a_fc5, size=1,name='c_out')
return value
def build_net(self,):
####### Actor Part
with fluid.unique_name.guard():
with fluid.program_guard(self.a_p, self.startup):
s = fluid.layers.data(name='current_state', shape=[3, 100, 100], dtype='float32')
td_error = fluid.layers.data(name='td_error', shape=[1], dtype='float32')
self.probs = self.build_a(s)
# define actor optimizer
self.prob_program = fluid.default_main_program().clone() # 1*8
lprobs = fluid.layers.log(self.probs) # log operation 1*8
log_prob = fluid.layers.reduce_max(lprobs, dim=1, keep_dim=True)
a_target = fluid.layers.reduce_mean(log_prob * td_error * -1.0)
optimizer_a = fluid.optimizer.Adam(learning_rate=0.001)
optimizer_a.minimize(a_target)
self.train_program_actor = fluid.default_main_program()
####### Critic Part
with fluid.unique_name.guard():
with fluid.program_guard(self.c_p, self.startup):
s = fluid.layers.data(name='current_state', shape=[3, 100, 100], dtype='float32')
s_ = fluid.layers.data(name='next_state', shape=[3, 100, 100], dtype='float32')
reward = fluid.layers.data(name='reward', shape=[1], dtype='float32')
reward = fluid.layers.clip(reward, min=-10.0, max=10.0)
v_curr = self.build_c(s)
v_next = self.build_c(s_)
self.c_target = fluid.layers.reduce_mean(reward + self.gamma * v_next - v_curr)
self.td_program = fluid.default_main_program().clone() # 1*1
# define while optimizer
optimizer_c = fluid.optimizer.SGD(learning_rate=0.0001)
optimizer_c.minimize(self.c_target)
self.train_program_critic = fluid.default_main_program()
place = fluid.CPUPlace()
self.exe = fluid.Executor(place)
# fluid exe
self.exe.run(self.startup)
def train(self, flag, current_state=None, next_state=None, reward=None, td_error=None):
current_state = np.array(current_state, dtype='float32')
current_state = np.expand_dims(current_state, axis=0)
print(current_state.shape)
if flag == 'a':
td_error = np.expand_dims(np.array(td_error, dtype='float32'), axis=0)
print(td_error.shape)
self.exe.run(self.a_p,
feed={
'current_state': current_state,
'td_error': td_error}
)
elif flag == 'c':
next_state = np.array(next_state, dtype='float32')
next_state = np.expand_dims(next_state, axis=0)
print(next_state.shape)
reward = np.expand_dims(np.array([reward], dtype='float32'), axis=0)
print(reward.shape)
self.exe.run(self.c_p,
feed={
'current_state': current_state,
'next_state': next_state,
'reward': reward}
)
class Actor(object):
def __init__(self, A_DIM=4, gamma=0.94):
self.A_DIM = A_DIM
self.prob = None
self.log_prob = None
self.prob_program = None
self.td_error = None
self.gamma = gamma
place = fluid.CPUPlace()
self.exe = fluid.Executor(place)
def get_input(self):
# create input
s = fluid.layers.data(name='current_state', shape=[3, 100, 100], dtype='float32')
td_error = fluid.layers.data(name='td_error', shape=[1], dtype='float32')
return s, td_error
def act(self, state):
state = np.array(state, dtype='float32')
state = np.expand_dims(state, axis=0)
act_probs = self.exe.run(self.prob_program, feed={'current_state': state}, fetch_list=[self.probs])[0]
return act_probs
def build_net(self):
s, td_error = self.get_input()
a_conv1 = conv_bn_layer(main_input=s, ch_out=32, filter_size=3, stride=2, padding=1)
a_conv2 = conv_bn_layer(main_input=a_conv1, ch_out=32, filter_size=3, stride=2, padding=1)
a_conv3 = conv_bn_layer(main_input=a_conv2, ch_out=32, filter_size=3, stride=2, padding=1)
a_conv4 = conv_bn_layer(main_input=a_conv3, ch_out=32, filter_size=3, stride=2, padding=1)
a_fc5 = fluid.layers.fc(input=a_conv4, size=50, act='relu')
self.probs = fluid.layers.fc(input=a_fc5, size=self.A_DIM, act='softmax')
self.prob_program = fluid.default_main_program().clone() # 1*8
lprobs = fluid.layers.log(self.probs) # log operation 1*8
log_prob = fluid.layers.reduce_max(lprobs, dim=1, keep_dim=True)
# log_prob.stop_gradient = True
neg_log_prob = fluid.layers.reduce_mean(log_prob * td_error * -1.0)
# define optimizer
optimizer = fluid.optimizer.Adam(learning_rate=0.0001)
optimizer.minimize(neg_log_prob)
# define program
self.train_program_actor = fluid.default_main_program()
# fluid exe
self.exe.run(fluid.default_startup_program())
def train(self, state, td_error):
self.exe.run(self.train_program_actor,
feed={
'current_state': state,
'td_error': td_error
})
class Critic(object):
def __init__(self, gamma=0.9):
self.td_error = None
self.gamma = gamma
self.c_scope = fluid.Scope()
place = fluid.CPUPlace()
self.exe = fluid.Executor(place)
def get_input(self):
# create input
s = fluid.layers.data(name='current_state', shape=[3, 100, 100], dtype='float32')
s_ = fluid.layers.data(name='next_state', shape=[3, 100, 100], dtype='float32')
self.reward = fluid.layers.data(name='reward', shape=[1], dtype='float32')
return s, s_
def get_td(self, current_state, next_state, reward):
current_state = np.array(current_state, dtype='float32')
current_state = np.expand_dims(current_state, axis=0)
next_state = np.array(next_state, dtype='float32')
next_state = np.expand_dims(next_state, axis=0)
reward = np.expand_dims(np.array([reward], dtype='float32'), -1)
td_error = self.exe.run(self.td_program,
feed={'current_state': current_state,
'next_state': next_state,
'reward': reward},
fetch_list=[self.td_error])[0]
return td_error
def build_net(self):
s, s_ = self.get_input()
# reward = fluid.layers.reduce_max(reward, dim=0)
reward = fluid.layers.clip(self.reward, min=-1.0, max=1.0)
print(reward.shape)
v_curr = self.predict_values(s)
print(v_curr.shape)
v_next = self.predict_values(s_)
print(v_next.shape)
# v_curr.stop_gradient = True
# v_next.stop_gradient = True
self.td_error = fluid.layers.reduce_mean(reward + self.gamma * v_next - v_curr)
print(self.td_error.shape)
self.td_program = fluid.default_main_program().clone() # 1*1
# define optimizer
optimizer = fluid.optimizer.Adam(learning_rate=0.0001)
optimizer.minimize(self.td_error)
# define program
self.train_program_critic = fluid.default_main_program()
# fluid exe
self.exe.run(fluid.default_startup_program())
def predict_values(self, s):
a_conv1 = conv_bn_layer(main_input=s, ch_out=32, filter_size=3, stride=2, padding=1)
a_conv2 = conv_bn_layer(main_input=a_conv1, ch_out=32, filter_size=3, stride=2, padding=1)
a_conv3 = conv_bn_layer(main_input=a_conv2, ch_out=32, filter_size=3, stride=2, padding=1)
a_conv4 = conv_bn_layer(main_input=a_conv3, ch_out=32, filter_size=3, stride=2, padding=1)
a_fc5 = fluid.layers.fc(input=a_conv4, size=256, act='relu')
value = fluid.layers.fc(input=a_fc5, size=1)
return value
def train(self, current_state, next_state, reward):
current_state = np.expand_dims(current_state, axis=0)
next_state = np.expand_dims(next_state, axis=0)
# reward = np.expand_dims(np.expand_dims(np.array(reward, dtype='float32'), axis=0), axis=0)
print(current_state.shape)
print(next_state.shape)
reward = np.expand_dims(np.array([reward], dtype='float32'), -1)
print(reward)
self.exe.run(self.train_program_critic,
feed={
'current_state': current_state,
'next_state': next_state,
'reward': reward}
)