-
Notifications
You must be signed in to change notification settings - Fork 12
/
power_triangle_gym.py
283 lines (240 loc) · 12.6 KB
/
power_triangle_gym.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
275
276
277
278
279
280
281
282
283
import gym
from gym import spaces
from EagarTsaiModel import EagarTsai as ET
import numpy as np
from stable_baselines3.common.env_checker import check_env
from matplotlib import pyplot as plt
import os
fig_dir = 'results/triangle_power_control_figures'
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
class EnvRLAM(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self, plot=False, frameskip=2, verbose=0):
# Define action and observation space
super(EnvRLAM, self).__init__()
# They must be gym.spaces objects
self.plot = plot
self.squaresize = 10
self.action_space = spaces.Box(low=np.array(
[-1]), high=np.array([1]), dtype=np.float16)
self.observation_space = spaces.Box(low=300, high=20000, shape=(
9, self.squaresize, self.squaresize,), dtype=np.float64)
self.ETenv = ET(20e-6, V=0.8, bc='flux')
if plot:
self.constantETenv = ET(20e-6, V=0.8, bc='flux')
self.velocity = []
self.current_step = 0
self.depths = []
self.times = []
self.power = []
self.hv = 0
self.current = 0
self.angle = 0
self.inddepth = []
self.indtimes = []
self.indpower = []
self.residual = False
self.reward = 0
self.timesteps = 0
self.frameskip = frameskip
self.verbose = verbose
self.total_steps = 0
buffer = np.stack((self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0],
self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize,
0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0],
self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0]))
self.buffer = (buffer - np.mean(buffer))
def step(self, action):
power = action[0]*250 + 250
time = 50e-6
for m in range(self.frameskip):
self.power.append(power)
self.timesteps += 1
V = 50e-6*0.8/time
self.velocity.append(V)
# breakpoint()
if self.current_step >= 12:
reward = self.reward/12 - 0.5 * \
(np.max(self.depths[20:]) - np.min(self.depths[20:]))/25e-6
minmax_reward = 0.5 * \
(np.max(self.depths[20:]) - np.min(self.depths[20:]))/25e-6
if self.verbose == 2:
print(reward, "reward", "velocity", V, "power", power,
"timestep", self.total_steps, "amplitude", minmax_reward)
print("Episode done")
if self.verbose == 1:
if self.total_steps % 1 == 0:
print(str(self.total_steps*8) +
" episodes run, current reward = " + str(reward))
if self.verbose == 0:
if self.total_steps % 10 == 0:
print(str(self.total_steps*8) +
" episodes run, current reward = " + str(reward))
done = True
self.total_steps += 1
else:
done = False
if self.residual:
V = (self.hv - self.current)*0.8/time
self.ETenv.forward(time, self.angle, V=V, P=power)
meltpool = self.ETenv.meltpool()
self.depths.append(meltpool)
self.times.append(self.ETenv.time)
reward = 1 - np.abs((55e-6 + meltpool)/25e-6)
self.reward += reward
self.current_step += 1
idxx = self.ETenv.location_idx[0]
idxy = self.ETenv.location_idx[1]
self.buffer[8, :, :] = np.copy(self.buffer[5, :, :])
self.buffer[7, :, :] = np.copy(self.buffer[4, :, :])
self.buffer[6, :, :] = np.copy(self.buffer[3, :, :])
self.buffer[5, :, :] = np.copy(self.buffer[2, :, :])
self.buffer[4, :, :] = np.copy(self.buffer[1, :, :])
self.buffer[3, :, :] = np.copy(self.buffer[0, :, :])
padsize = self.squaresize
theta_pad = np.copy(np.pad(self.ETenv.theta, ((
padsize, padsize), (padsize, padsize), (0, 0)), mode='reflect'))
try:
self.buffer[2, :, :] = theta_pad[padsize+idxx-self.squaresize//2:padsize+idxx +
self.squaresize//2, padsize+idxy-self.squaresize//2:padsize+idxy+self.squaresize//2, -1]
self.buffer[1, :, :] = theta_pad[padsize+idxx-self.squaresize //
2:padsize+idxx+self.squaresize//2, padsize+idxy, 0:self.squaresize]
self.buffer[0, :, :] = theta_pad[padsize+idxx, padsize+idxy -
self.squaresize//2:padsize+idxy+self.squaresize//2, 0:self.squaresize]
except:
breakpoint()
self.buffer[0:3] = (self.buffer[0:3] - np.mean(self.buffer[0:3], axis=(1, 2))[
:, None, None])/(np.std(self.buffer[0:3], axis=(1, 2))[:, None, None] + 1e-10)
obs = self.buffer
self.residual = False
self.hv = 0
self.current = 0
return obs, reward, done, {}
else:
idx = self.current_step - 1
if idx < 0:
idx = 0
self.hv = (750e-6 - idx*70e-6)
dtprime = 0
angle = (self.current_step % 3)*2*np.pi/3
step = 50e-6*0.8
self.angle = angle
self.current += step
self.ETenv.forward(time, angle, V=V, P=power)
dtprime += step
meltpool = self.ETenv.meltpool()
self.depths.append(meltpool)
self.times.append(self.ETenv.time)
reward = 1 - np.abs((55e-6 + meltpool)/25e-6)
self.reward += reward
if ((self.hv - self.current) < 50e-6*0.8 and (self.hv - self.current) > 0):
self.residual = True
if m == 0:
self.inddepth.append(meltpool)
self.indtimes.append(self.ETenv.time)
if self.plot:
np.savetxt(
fig_dir + "/" + "powercontroltriangletimesnorm", np.array(self.times)*1e3)
np.savetxt(fig_dir + "/" + "powercontroltriangledepthsframeskip" +
str(self.frameskip), np.array(self.depths))
np.savetxt(fig_dir + "/" + "powercontroltrianglevelocityframeskip" +
str(self.frameskip), np.array(self.velocity))
np.savetxt(fig_dir + "/" + "powercontroltrianglepowerframeskip" +
str(self.frameskip), np.array(self.power))
testfigs = self.ETenv.plot()
testfigs[0].savefig(fig_dir + "/" + str(self.frameskip) +
'powercontroltriangle_test' + '%04d' % self.timesteps + ".png")
plt.clf()
plt.plot(np.array(self.times)*1e3, np.array(self.depths)*1e6)
plt.ylim(-120, 10)
plt.xlabel(r'Time, $t$ [ms]')
plt.ylabel(r'Melt Depth, $d$, [$\mu$m]')
plt.plot(np.array(self.indtimes)*1e3,
np.array(self.inddepth)*1e6, 'k.')
plt.xlim(0, 0.008*1e3)
plt.title(str(round(self.ETenv.time*1e6)) + r'[$\mu$s] ')
plt.plot(np.arange(0, 0.015*1e3, 0.01), -55 *
np.ones(len(np.arange(0, 0.015*1e3, 0.01))), 'k--')
plt.savefig(fig_dir + "/" + str(self.frameskip) +
'powercontroltriangletestdepth' + '%04d' % self.timesteps + ".png")
plt.clf()
plt.plot(np.array(self.times)*1e3, self.velocity)
plt.xlabel(r'Time, $t$ [ms]')
plt.ylabel(r'Velocity, $V$, [m/s]')
plt.xlim(0, 0.008*1e3)
plt.ylim(0, 10.0)
plt.title(str(round(self.ETenv.time*1e6)) + r'[$\mu$s] ')
plt.savefig(fig_dir + "/" + str(self.frameskip) +
'powercontroltriangletestvelocity' + '%04d' % self.timesteps + ".png")
plt.clf()
plt.close('all')
plt.plot(np.array(self.times)*1e3, self.power)
plt.xlabel(r'Time, $t$ [ms]')
plt.ylabel(r'Power, $P$, W')
plt.xlim(0, 0.008*1e3)
plt.ylim(0, 500.0)
plt.title(str(round(self.ETenv.time*1e6)) + r'[$\mu$s] ')
plt.savefig(fig_dir + "/" + str(self.frameskip) +
'powercontroltriangletestpower' + '%04d' % self.timesteps + ".png")
plt.clf()
plt.close('all')
interv = 2
idxx = self.ETenv.location_idx[0]
idxy = self.ETenv.location_idx[1]
self.buffer[8, :, :] = np.copy(self.buffer[5, :, :])
self.buffer[7, :, :] = np.copy(self.buffer[4, :, :])
self.buffer[6, :, :] = np.copy(self.buffer[3, :, :])
self.buffer[5, :, :] = np.copy(self.buffer[2, :, :])
self.buffer[4, :, :] = np.copy(self.buffer[1, :, :])
self.buffer[3, :, :] = np.copy(self.buffer[0, :, :])
padsize = self.squaresize
theta_pad = np.copy(np.pad(self.ETenv.theta, ((
padsize, padsize), (padsize, padsize), (0, 0)), mode='reflect'))
try:
self.buffer[2, :, :] = theta_pad[padsize+idxx-self.squaresize//2:padsize+idxx +
self.squaresize//2, padsize+idxy-self.squaresize//2:padsize+idxy+self.squaresize//2, -1]
self.buffer[1, :, :] = theta_pad[padsize+idxx-self.squaresize //
2:padsize+idxx+self.squaresize//2, padsize+idxy, 0:self.squaresize]
self.buffer[0, :, :] = theta_pad[padsize+idxx, padsize+idxy -
self.squaresize//2:padsize+idxy+self.squaresize//2, 0:self.squaresize]
except Exception as e:
print(e)
breakpoint()
self.buffer[0:3] = (self.buffer[0:3] - np.mean(self.buffer[0:3], axis=(1, 2))[
:, None, None])/(np.std(self.buffer[0:3], axis=(1, 2))[:, None, None] + 1e-10)
obs = self.buffer
return obs, reward, done, {}
# Execute one time step within the environment
def reset(self):
self.ETenv.reset()
self.current_step = 0
self.hv = 0
self.current = 0
self.angle = 0
self.residual = False
step = 2
self.reward = 0
self.timesteps = 0
buffer = np.stack((self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0],
self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize,
0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0],
self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0], self.ETenv.theta[0:self.squaresize, 0:self.squaresize, 0]))
self.buffer = (buffer - np.mean(buffer))
return np.copy(self.buffer)
# Reset the state of the environment to an initial state
def render(self, mode='console', close=False):
pass
def plot(self):
plt.close('all')
self.ax.plot(self.times, self.depths)
plt.xlabel(r'Time, $t$ [ms]')
plt.ylabel(r'Melt Depth, $d$, [$\mu$m]')
plt.close('all')
return self.fig
# Render the environment to the screen
def main():
env = EnvRLAM()
# It will check your custom environment and output additional warnings if needed
check_env(env)