-
Notifications
You must be signed in to change notification settings - Fork 2
/
ppo_train.py
274 lines (208 loc) · 8.45 KB
/
ppo_train.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
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
#!/usr/bin/python3
import gym
import numpy as np
import tensorflow as tf
from policy_net import Policy_net
from ppo import PPOTrain
import roslib
import rospy
import random
import time
import math
import csv
from std_srvs.srv import Empty
from gazebo_msgs.srv import SetModelConfiguration
from control_msgs.msg import JointControllerState
from sensor_msgs.msg import JointState
from gazebo_msgs.msg import LinkStates
from gazebo_msgs.srv import SetLinkState
from gazebo_msgs.msg import LinkState
from std_msgs.msg import Float64
from std_msgs.msg import String
from sensor_msgs.msg import Joy
ENV_NAME = 'Cartpole_v0'
ITERATION = 2000
GAMMA = 0.95
pubCartPosition = rospy.Publisher('/stand_cart_position_controller/command', Float64, queue_size=1)
pubJointStates = rospy.Publisher('/joint_states', JointState, queue_size=1)
reset_world = rospy.ServiceProxy('/gazebo/reset_world', Empty)
reset_joints = rospy.ServiceProxy('/gazebo/set_model_configuration', SetModelConfiguration)
unpause = rospy.ServiceProxy('/gazebo/unpause_physics', Empty)
pause = rospy.ServiceProxy('/gazebo/pause_physics', Empty)
set_link = rospy.ServiceProxy('/gazebo/set_link_state', SetLinkState)
fall = 0
rospy.init_node('cartpole_control_script')
rate = rospy.Rate(120)
class RobotState(object):
def __init__(self):
self.cart_x = 0.0
self.cart_x_dot = 0.0
self.pole_theta = 0.0
self.pole_theta_dot = 0.0
self.robot_state = [self.cart_x, self.cart_x_dot, self.pole_theta, self.pole_theta_dot]
self.data = None
self.latest_reward = 0.0
self.fall = 0
self.theta_threshold = 0.20943951023
self.x_threshold = 0.4
self.current_vel = 0.0
self.done = False
robot_state = RobotState()
def reset():
rospy.wait_for_service('/gazebo/reset_world')
try:
reset_world()
except (rospy.ServiceException) as e:
print ('reset_world failed!')
# rospy.wait_for_service('/gazebo/reset_world')
rospy.wait_for_service('/gazebo/set_model_configuration')
try:
#reset_proxy.call()
# reset_world()
reset_joints("cartpole", "robot_description", ["stand_cart", "cart_pole"], [0.0, 0.0])
except (rospy.ServiceException) as e:
print ('/gazebo/reset_joints service call failed')
rospy.wait_for_service('/gazebo/pause_physics')
try:
pause()
except (rospy.ServiceException) as e:
print ('rospause failed!')
# rospy.wait_for_service('/gazebo/unpause_physics')
# try:
# unpause()
# except (rospy.ServiceException) as e:
# print "/gazebo/pause_physics service call failed"
set_robot_state()
robot_state.current_vel = 0
print ('called reset()')
def set_robot_state():
robot_state.robot_state = [robot_state.cart_x, robot_state.cart_x_dot, robot_state.pole_theta, robot_state.pole_theta_dot]
def take_action(action):
rospy.wait_for_service('/gazebo/unpause_physics')
try:
unpause()
except (rospy.ServiceException) as e:
print ('/gazebo/pause_physics service call failed')
if action == 1:
robot_state.current_vel = robot_state.current_vel + 0.05
else:
robot_state.current_vel = robot_state.current_vel - 0.05
# print "publish : ", robot_state.current_vel
pubCartPosition.publish(robot_state.current_vel)
reward = 1
# ['cart_pole', 'stand_cart']
if robot_state.data==None:
while robot_state.data is None:
try:
robot_state.data = rospy.wait_for_message('/joint_states', JointState, timeout=5)
except:
print ('Error getting /joint_states data.')
set_robot_state()
if robot_state.cart_x < -robot_state.x_threshold or robot_state.cart_x > robot_state.x_threshold or robot_state.pole_theta > robot_state.theta_threshold \
or robot_state.pole_theta < -robot_state.theta_threshold:
robot_state.done = True
reward = 1
else:
reward = 1
# rate.sleep()
return reward, robot_state.done
def callbackJointStates(data):
if len(data.velocity) > 0:
robot_state.cart_x_dot = data.velocity[1]
robot_state.pole_theta_dot = data.velocity[0]
else:
robot_state.cart_x_dot = 0.0
robot_state.pole_theta_dot = 0.0
robot_state.cart_x = data.position[1]
robot_state.pole_theta = data.position[0]
set_robot_state()
# print ('DATA :'), data
def listener():
print ('listener')
rospy.Subscriber("/joint_states", JointState, callbackJointStates)
def main():
listener()
# env = gym.make('CartPole-v0')
# env.seed(0)
ob_space = 4
Policy = Policy_net('policy')
Old_Policy = Policy_net('old_policy')
PPO = PPOTrain(Policy, Old_Policy, gamma=GAMMA)
saver = tf.train.Saver()
with tf.Session() as sess:
writer = tf.summary.FileWriter('./log/train', sess.graph)
sess.run(tf.global_variables_initializer())
reset()
obs = robot_state.robot_state
reward = 0
success_num = 0
for iteration in range(ITERATION): # episode
observations = []
actions = []
v_preds = []
rewards = []
run_policy_steps = 0
while True: # run policy RUN_POLICY_STEPS which is much less than episode length
run_policy_steps += 1
obs = np.stack([obs]).astype(dtype=np.float32) # prepare to feed placeholder Policy.obs
act, v_pred = Policy.act(obs=obs, stochastic=True)
print('act: ',act, 'v_pred: ',v_pred )
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
observations.append(obs)
actions.append(act)
v_preds.append(v_pred)
rewards.append(reward)
reward, done = take_action(act)
time.sleep(0.25)
next_obs = robot_state.robot_state
if done:
v_preds_next = v_preds[1:] + [0] # next state of terminate state has 0 state value
reset()
obs = robot_state.robot_state
reward = -1
break
else:
obs = next_obs
writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_length', simple_value=run_policy_steps)])
, iteration)
writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_reward', simple_value=sum(rewards))])
, iteration)
if sum(rewards) >= 195:
success_num += 1
render = True
if success_num >= 100:
saver.save(sess, './model/model.ckpt')
print('Clear!! Model saved.')
break
else:
success_num = 0
gaes = PPO.get_gaes(rewards=rewards, v_preds=v_preds, v_preds_next=v_preds_next)
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations, [len(observations), 4])
actions = np.array(actions).astype(dtype=np.int32)
rewards = np.array(rewards).astype(dtype=np.float32)
v_preds_next = np.array(v_preds_next).astype(dtype=np.float32)
gaes = np.array(gaes).astype(dtype=np.float32)
gaes = (gaes - gaes.mean())
print('gaes', gaes)
PPO.assign_policy_parameters()
inp = [observations, actions, rewards, v_preds_next, gaes]
# train
for epoch in range(4):
sample_indices = np.random.randint(low=0, high=observations.shape[0], size=64) # indices are in [low, high)
sampled_inp = [np.take(a=a, indices=sample_indices, axis=0) for a in inp] # sample training data
PPO.train(obs=sampled_inp[0],
actions=sampled_inp[1],
rewards=sampled_inp[2],
v_preds_next=sampled_inp[3],
gaes=sampled_inp[4])
summary = PPO.get_summary(obs=inp[0],
actions=inp[1],
rewards=inp[2],
v_preds_next=inp[3],
gaes=inp[4])[0]
writer.add_summary(summary, iteration)
writer.close()
if __name__ == '__main__':
main()