forked from clvoloshin/constrained_batch_policy_learning
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathplot_policy_improvement_v2.py
343 lines (275 loc) · 14.5 KB
/
plot_policy_improvement_v2.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
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
import deepdish as dd
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.lines as mlines
from matplotlib.legend import Legend
import seaborn as sns; sns.set(color_codes=True)
import os
import scipy.signal as signal
# Colors
alpha = 0.15
sns.set(style="whitegrid", palette="Paired")
colorSet = sns.color_palette("Paired", 10);
def discounted_sum(costs, discount):
'''
Calculate discounted sum of costs
'''
y = signal.lfilter([1], [1, -discount], x=costs[::-1])
return y[::-1][0]
def color_gen():
colors = [ "dusty purple", "windows blue", "faded green", "dark pink", "amber"]
colors = sns.xkcd_palette(colors)
idx = -1
while 1:
idx = (idx + 1) % len(colors)
yield colors[idx]
# Data setup
dones = dd.io.load(os.path.join('seed_2_data', 'car_data_is_done_seed_2.h5'))
costs = dd.io.load(os.path.join('seed_2_data', 'car_data_rewards_seed_2.h5'))
dones = np.hstack([0,1+np.where(dones)[0]])
episodes = []
for low_, high_ in zip(dones[:-1], dones[1:]):
new_episode ={
'c': costs[low_:high_, 0].reshape(-1),
'brake': costs[low_:high_, -1].reshape(-1),
'lane': costs[low_:high_, 3].reshape(-1),
}
episodes.append(new_episode)
discounted_costs = np.array([[discounted_sum(x['c'],.95),discounted_sum(x['brake'],.95),discounted_sum(x['lane'],.95)] for x in episodes])
data = dd.io.load('car_policy_improvement.h5')
DQN = [-39.61397106365249, 7.703194041056963, 115.62071639160499]
LSPI = pd.read_csv('lspi_results.csv')
plt.rc('text', usetex=True)
lines, fill_betweens= [], []
plt.rc_context({'axes.edgecolor':'k'})
fig = plt.figure(figsize=(12, 6))
grid = plt.GridSpec(6, 4, wspace=0.6, hspace=0.5)
ax1 = fig.add_subplot(grid[0:4, :2])
# fig, ax1 = plt.subplots()
ax1.grid(alpha=.35)
max_iterations = 27
iterations = range(len(data['g_eval'][0][:max_iterations]))
colors = color_gen()
constraint_names = ['Braking', 'Center of Lane']
constraint_upper_bound = [5.8, 85.]#[1.5, 5.]
locations = ['lower left', 'lower center', 'lower right']
fontsize = 16
legend_fontsize = 16
legend_title_fontsize = 16
major_tick_mark_size = 14
def derandomize(data, constraints, min_iteration):
fqe_c = np.array(data['c_eval'][0])[:,-1]
fqe_g_0 = np.array(data['g_eval'][0])[:,-1]
fqe_g_1 = np.array(data['g_eval'][1])[:,-1]
out = []
for iteration in range(min_iteration, len(fqe_c)):
df_tmp = pd.DataFrame(np.hstack([np.arange(min_iteration,iteration+1).reshape(1,-1).T, fqe_c[min_iteration:(iteration+1)].reshape(1,-1).T, fqe_g_0[min_iteration:(iteration+1)].reshape(1,-1).T, fqe_g_1[min_iteration:(iteration+1)].reshape(1,-1).T ]), columns=['iteration', 'fqe_c', 'fqe_g_0', 'fqe_g_1'])
df_tmp = df_tmp[(df_tmp['fqe_g_0'] < constraints[0]) & (df_tmp['fqe_g_1'] < constraints[1]) ]
try:
argmin = np.argmin(np.array(df_tmp['fqe_c']))
it = int(df_tmp.iloc[argmin]['iteration'])
except:
argmin = 0
it = 0
out.append(np.hstack([iteration, np.hstack([data['c_exacts'][it], np.array(data['g_exacts'])[it,:-1]]) ]))
return pd.DataFrame(out, columns=['iterations', 'c_derandomized', 'g_0_derandomized', 'g_1_derandomized'])
df_derandom = derandomize(data, np.array(constraint_upper_bound)*.8, 0)
legend = []
car_color = colors.next()
derandom_color = colors.next()
c_values = np.array(data['c_eval_actuals'])[:max_iterations,-10:,:] # shape = (iteration #, k, performance)
last = np.cumsum(c_values[:,-1,0])/np.arange(1,1+len(c_values[:,-1,0]))#*100
evaluation = np.array(pd.DataFrame(c_values[:,-1,0]).expanding().mean()).reshape(-1)
std = np.array(pd.DataFrame(c_values[:,-1,0]).expanding().std()).reshape(-1)
#evaluation = np.mean(c_values, axis=1)[:,0]#*100
#std = np.std(c_values, axis=1)[:,0]#*100
lines.append( ax1.plot(iterations, last, color = car_color,linestyle='-',markersize=7, label='Exact') )
lines.append( ax1.plot(df_derandom['iterations'], df_derandom['c_derandomized'], color = derandom_color,linestyle='-',markersize=7, label='Exact') )
# lines.append( ax1.plot(iterations, evaluation, color = car_color,marker='s',markersize=7, label='Mean of last 10') )
# fill_betweens.append( ax1.fill_between(iterations,evaluation-std, evaluation+std, alpha = alpha, color = car_color, zorder = 10) )
y_err_lower = last - np.min(c_values, axis=1)[:,0]
y_err_higher = last - np.max(c_values, axis=1)[:,0]
legend.append( mlines.Line2D([], [], color=car_color, linestyle='-',
markersize=7, label='Percent of Track Covered') )
legend.append( mlines.Line2D([], [], color=derandom_color, linestyle='-',
markersize=7, label='Percent of Track Covered') )
# legend.append( mlines.Line2D([], [], color=car_color, marker='s',
# markersize=7, label='Percent of Track Covered') )
## Baselines
# LSPI
lspi_color = colors.next()
lspi = np.array(LSPI.iloc[:,0])
evaluation = np.array(pd.DataFrame(lspi).expanding().mean()).reshape(-1)
lines.append( ax1.plot(iterations, evaluation, color = lspi_color,markersize=7,linestyle='-' , label='Exact') )
legend.append( mlines.Line2D([], [], color=lspi_color, linestyle='-' ,
markersize=7, label='Percent of Track Covered') )
# DQN
dqn_color = colors.next()
dqn = np.array([DQN[0]]*len(last))
evaluation = np.array(pd.DataFrame(dqn).expanding().mean()).reshape(-1)
lines.append( ax1.plot(iterations, evaluation, color = dqn_color,markersize=7,linestyle='-' , label='Exact') )
legend.append( mlines.Line2D([], [], color=dqn_color, linestyle='-' ,
markersize=7, label='Percent of Track Covered') )
# Pi_D
pi_d_color = colors.next()
evaluation = np.mean(discounted_costs[:,0]).reshape(-1)
lines.append( ax1.plot(iterations, [evaluation]*len(iterations), color = pi_d_color,markersize=7,linestyle='-' , label='Exact') )
legend.append( mlines.Line2D([], [], color=pi_d_color, linestyle='-' ,
markersize=7, label='Percent of Track Covered') )
legend.append( mlines.Line2D([], [], color='k', linestyle='--' ,
markersize=7, label='Percent of Track Covered') )
ax1.set_xlabel('Iteration (t)', fontsize=fontsize)
ax1.set_ylabel('Value (Main Objective)', color='k', fontsize=fontsize+2)
ax1.tick_params(axis='y', labelcolor='k')
ax1.set_ylim(bottom=-55, top=-15)
# ax1.set_ylim(bottom=20, top=55)
ax1.set_xlim(-.5, 25)
labels = np.array(['FQE', 'Algo 2', 'Mean', 'Regularized LSPI', 'Online-RL \n(no constraint)', 'Algo 2 \n(Derandomized)', r'$\pi_D$', 'Constraint Threshold'])
leg = Legend(ax1,
np.array(legend)[[0,1,3,2,4,5]],
labels[[1,5,4,3,6,7]],
loc='lower left',
bbox_to_anchor=(.05,.02),
bbox_transform=fig.transFigure,
ncol = 2,
frameon=True,
fontsize = legend_fontsize-1)
ax1.add_artist(leg)
plt.tick_params(axis='both', which='major', labelsize=major_tick_mark_size)
plt.tick_params(axis='both', which='minor', labelsize=8)
ax1.set_title('Main Objective - Accumulated Cost', fontsize=fontsize+2)
# plt.subplots_adjust(right=0.7)
# plt.tight_layout()#rect=[0,.2,1,1])
# plt.tight_layout(rect=[-.025,-.025,.675,1.025])
# plt.savefig('car_main_value_wo_band.png', format='png', dpi=300)
# plt.show()
# import pdb; pdb.set_trace()
# car_color = colors.next()
# c_values = np.array(data['c_eval'][0])[:,-10:] # shape = (iteration #, k)
# last = np.mean(c_values, axis=1)#c_values[:,-1,0]#*100
# evaluation = np.mean(c_values, axis=1)#*100
# std = np.std(c_values, axis=1)#*100
# lines.append( ax1.plot(iterations, last, color = car_color,marker='o',markersize=7, label='Percent of Track Covered') )
# fill_betweens.append( ax1.fill_between(iterations,evaluation-std, evaluation+std, alpha = alpha, color = car_color, zorder = 10) )
# legend.append( mlines.Line2D([], [], color=car_color, marker='o',
# markersize=7, label='Percent of Track Covered') )
#labels = np.array([r"$FQE: \;\; \frac{1}{10}\sum_{i=40}^{50}\widehat{C^{i}}(\pi_{Q_{50}})$", r"$Exact: \;\; C(\pi_{Q_{50}})$", r'$Mean: \;\; \frac{1}{10}\sum_{i=40}^{50} C(\pi_{Q_i})$'])
# labels = np.array(['FQE', r'$Our \; C$', 'Mean', r'$DQN \; C$', r'$LSPI \; C$'])
# leg = Legend(ax1, np.array(legend)[[0,1,2]], labels[[1,3,4]], title='Main Objective', loc=locations[0], frameon=False, fontsize = legend_fontsize) #shadow=True, fancybox=True,
# ax1.add_artist(leg)
# ax = ax1.twinx()
# tex_labels = [[r'$FQE: \;\; \frac{1}{10}\sum_{i=40}^{50} \widehat{G^{i}_0}(\pi_{Q_{50}})$', r'$Exact: \;\; G_0(\pi_{Q_{50}})$'],
# [r'$FQE: \;\; \frac{1}{10}\sum_{i=40}^{50} \widehat{G^{i}_1}(\pi_{Q_{50}})$', r'$Exact: \;\; G_1(\pi_{Q_{50}})$']]
# tex_labels = [[r'$FQE \; G_0$', r'$Our \; G_0$'], [r'$FQE \; G_1$', r'$Our \; G_1$']]
# baseline_labels = [[r'$DQN \; G_0$', r'$LSPI \; G_0$'], [r'$DQN \; G_1$', r'$LSPI \; G_1$']]
tex_labels = [[r'$FQE \; G_0$', 'Algo 2', 'Algo 2 (Derandomized)'], [r'$FQE \; G_1$', 'Algo 2', 'Algo 2 (Derandomized)']]
# baseline_labels = [['DDQN', 'LSPI'], ['DDQN', 'LSPI']]
baseline_labels = [['Online-RL (no constraint)', 'Regularized LSPI', r'$\pi_D$'], ['Online-RL (no constraint)', 'Regularized LSPI', r'$\pi_D$']]
# plt.clf()
# fig, axs = plt.subplots(2, sharex=True)
axs = []
axs.append(fig.add_subplot(grid[:3, 2:]))
axs.append(fig.add_subplot(grid[3:, 2:]))
for idx in data['g_eval'].keys():
colors = color_gen()
ax = axs[idx]
ax.grid(alpha=.35)
legend = []
# FQE
constraint = np.array(data['g_eval'][idx]) # shape = (iteration #, k) referring to Q_k
constraint = constraint[:max_iterations,-10:]
# evaluation = np.array(pd.DataFrame(np.mean(constraint, axis = 1)/constraint_upper_bound[idx]).expanding().mean()).reshape(-1)
# std = np.array(pd.DataFrame(np.mean(constraint, axis = 1)/constraint_upper_bound[idx]).expanding().std()).reshape(-1)
# evaluation = np.mean(constraint, axis = 1)/constraint_upper_bound[idx]
# std = np.std(constraint, axis=1)/constraint_upper_bound[idx]
label = constraint_names[idx]
# lines.append( ax.plot(iterations, evaluation, color = color,marker='o',markersize=7, label=tex_labels[idx][0]) )
# fill_betweens.append( ax.fill_between(iterations,evaluation-std, evaluation+std, alpha = alpha, color = color, zorder = 11+2*idx) )
#legend.append( mlines.Line2D([], [], color=color, marker='o',
# markersize=7, label=tex_labels[idx][0]) )
# EXACT
g_exacts = np.array(data['g_exacts'])[:max_iterations,idx]#/constraint_upper_bound[idx]
evaluation = np.array(pd.DataFrame(g_exacts).expanding().mean()).reshape(-1)
std = np.array(pd.DataFrame(g_exacts).expanding().std()).reshape(-1)
lines.append( ax.plot(iterations, evaluation, color = car_color ,linestyle='-' ,linewidth=2.0, label=tex_labels[idx][1]) )
legend.append( mlines.Line2D([], [], color=car_color, linestyle='-' ,linewidth=2.0,
markersize=7, label=tex_labels[idx][1]) )
# Derandomized
lines.append( ax.plot(df_derandom['iterations'], df_derandom['g_%s_derandomized' % idx], linewidth=2.0,color = derandom_color,linestyle='-',markersize=7, label='Exact') )
legend.append( mlines.Line2D([], [], color=derandom_color, linestyle='-' ,linewidth=2.0,
markersize=7, label=tex_labels[idx][2]) )
# fill_betweens.append( ax.fill_between(iterations,evaluation-std, evaluation+std, alpha = alpha, color = color, zorder = 11+2*idx+1) )
## BASELINES
# fill_betweens.append( ax.fill_between(iterations,evaluation-std, evaluation+std, alpha = alpha, color = color, zorder = 11+2*idx+1) )
# LSPI
baseline = np.array(LSPI.iloc[:,idx+1])*constraint_upper_bound[idx]
evaluation = np.array(pd.DataFrame(baseline).expanding().mean()).reshape(-1)
std = np.array(pd.DataFrame(baseline).expanding().std()).reshape(-1)
lines.append( ax.plot(iterations, evaluation, color = lspi_color , linestyle='-', linewidth=2.0,label=baseline_labels[idx][1]) )
legend.append( mlines.Line2D([], [], color=lspi_color, linestyle='-', linewidth=2.0,
markersize=7, label=baseline_labels[idx][1]) )
# fill_betweens.append( ax.fill_between(iterations,evaluation-std, evaluation+std, alpha = alpha, color = color, zorder = 11+2*idx+1) )
# DQN
baseline = np.array([DQN[idx+1]]*len(evaluation))#/constraint_upper_bound[idx]
evaluation = np.array(pd.DataFrame(baseline).expanding().mean()).reshape(-1)
std = np.array(pd.DataFrame(baseline).expanding().std()).reshape(-1)
lines.append( ax.plot(iterations, evaluation, color = dqn_color , linestyle='-' ,linewidth=2.0, label=baseline_labels[idx][0]) )
legend.append( mlines.Line2D([], [], color=dqn_color, linestyle='-',linewidth=2.0,
markersize=7, label=baseline_labels[idx][0]) )
# Pi_D
evaluation = np.mean(discounted_costs[:,idx+1]).reshape(-1)
lines.append( ax.plot(iterations, [evaluation]*len(iterations), color = pi_d_color,markersize=7,linewidth=2.0,linestyle='-' , label=baseline_labels[idx][2]) )
legend.append( mlines.Line2D([], [], color=pi_d_color, linestyle='-' ,linewidth=2.0,
markersize=7, label=baseline_labels[idx][2]) )
# THRESHOLD
constraint_violation = [constraint_upper_bound[idx]]*len(iterations)
lines.append( ax.plot(iterations, constraint_violation, color = 'k', linestyle=':', linewidth=2.0, marker=None) )
legend.append( mlines.Line2D([], [], color='k', linestyle=':', marker=None, linewidth=2.0, label='Constraint Threshold') )
labels = []
for i in range(len(legend)):
line = legend[i]
try:
labels += [line.label]
except:
labels += [line.get_label()]
if idx == 1:
# leg = Legend(ax,
# legend,
# labels,
# title=label,
# # loc='center left',
# bbox_to_anchor=(0.5, -0.05),
# ncol = 3,
# frameon=False,
# # bbox_to_anchor=(1, 0.5),
# fontsize = legend_fontsize-3)
leg = Legend(ax,
legend,
labels,
loc='lower center',
bbox_to_anchor=(.5,0),
bbox_transform=fig.transFigure,
ncol = 2,
frameon=True,
fontsize = legend_fontsize-2)
plt.setp(leg.get_title(),fontsize='%s' % legend_title_fontsize)
# ax.add_artist(leg)
if idx == 1:
ax.set_xlabel('Iteration (t)', fontsize=fontsize)
lab = ['Value (Braking)', 'Value (Lane Center)'][idx]
ax.set_ylabel(lab, color='k', fontsize=fontsize+2)
# ax.set_ylim(bottom=-1, top=3)
ax.tick_params(axis='y', labelcolor='k')
if idx == 0: ax.set_ylim(bottom=-2, top=15)
ax.set_xlim(-.5, 25)
axs[0].tick_params(axis='both', which='major', labelsize=major_tick_mark_size)
axs[0].set_xticklabels([])
axs[1].tick_params(axis='both', which='major', labelsize=major_tick_mark_size)
axs[0].set_title('Constraints - Accumulated Cost', fontsize=fontsize+2)
plt.tight_layout(rect=[-.025,-.025,1.025,1.025])#rect=[0,.2,1,1])
plt.savefig('car_all_values_wo_band.png', format='png', dpi=300)
plt.show()
import pdb; pdb.set_trace()