-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathvarying_alpha_epsilon_experiment.py
241 lines (199 loc) · 12 KB
/
varying_alpha_epsilon_experiment.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
import numpy as np
from mdp_matrix import GridWorld, StochasticGridWorld, WindyGridCliffMazeWorld
from double_sarsa import double_sarsa
from n_step_sarsa import n_step_sarsa
from n_step_expected_sarsa import n_step_expected_sarsa
from n_step_tree_backup import n_step_tree_backup
from q_sigma import n_step_q_sigma
from expected_sarsa import expected_sarsa
from double_expected_sarsa import double_expected_sarsa
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sarsa import sarsa
from joblib import Parallel, delayed
test_rewards = [[i, j, -1.0] for i in range(5) for j in range(5)]
test_rewards[24] = [4, 4, 50]
terminal_states = [24]
obstacles = [[i, j, 0] for i in range(5) for j in range(5)]
obstacles[1*5+1] = [1, 1, 1]
obstacles[2*5+2] = [2, 2, 1]
obstacles[2*5+3] = [2, 3, 1]
obstacles[3*5+0] = [3, 0, 1]
obstacles[4*5+3] = [4, 3, 1]
traps = [0]*100
traps[1*5+2] = 1
traps[1*5+3] = 1
start_state = [0, 0]
gw = WindyGridCliffMazeWorld(5, test_rewards, terminal_states, traps, start_state, obstacles)
# print test_rewards
average_reward_double_sarsa = []
all_rewards_per_episode_double_sarsa = []
q_var_double_sarsa = []
average_reward_expected_sarsa = []
all_rewards_per_episode_expected_sarsa = []
q_var_expected_sarsa = []
average_reward_double_expected_sarsa = []
all_rewards_per_episode_double_expected_sarsa = []
q_var_double_expected_sarsa = []
average_reward_sarsa = []
all_rewards_per_episode_sarsa = []
q_var_sarsa = []
average_reward_n_step_sarsa = []
all_rewards_per_episode_n_step_sarsa = []
q_var_n_step_sarsa = []
average_reward_n_step_expected_sarsa = []
all_rewards_per_episode_n_step_expected_sarsa = []
q_var_n_step_expected_sarsa = []
average_reward_n_step_tree_backup= []
all_rewards_per_episode_n_step_tree_backup = []
q_var_n_step_tree_backup = []
average_reward_n_step_q_sigma= []
all_rewards_per_episode_n_step_q_sigma = []
q_var_n_step_q_sigma = []
epsilon = .1
n=10000
alphas = [x for x in np.arange(0.0, 1., .1)]
alphas[0] = .01
# import pdb; pdb.set_trace()
number_of_runs = 5
for r in range(number_of_runs):
for alpha in alphas:
print(alpha)
Q, average_reward, max_reward, all_rewards, Q_variances = double_sarsa(gw, n, epsilon=epsilon, alpha=alpha)
average_reward_double_sarsa.append(average_reward)
all_rewards_per_episode_double_sarsa.append(all_rewards)
q_var_double_sarsa.append(Q_variances)
print("Done double sarsa")
Q, average_reward, max_reward, all_rewards, Q_variances = expected_sarsa(gw, n, epsilon=epsilon, alpha=alpha)
average_reward_expected_sarsa.append(average_reward)
q_var_expected_sarsa.append(Q_variances)
print("Done expected sarsa")
all_rewards_per_episode_expected_sarsa.append(all_rewards)
Q, average_reward, max_reward, all_rewards, Q_variances = double_expected_sarsa(gw, n, epsilon=epsilon, alpha=alpha)
average_reward_double_expected_sarsa.append(average_reward)
q_var_double_expected_sarsa.append(Q_variances)
print("Done double expected sarsa")
all_rewards_per_episode_double_expected_sarsa.append(all_rewards)
Q, average_reward, max_reward, all_rewards, Q_variances = sarsa(gw, n, epsilon=epsilon, alpha=alpha)
q_var_sarsa.append(Q_variances)
average_reward_sarsa.append(average_reward)
all_rewards_per_episode_sarsa.append(all_rewards)
print("Done sarsa")
n_step_sarsa_results = Parallel(n_jobs=-2, verbose=10)(delayed(n_step_sarsa)(gw, n, alpha, gamma, epsilon, n=4) for alpha in alphas)
# Q, average_reward, max_reward, all_rewards, Q_variances = n_step_sarsa(gw, max_episode, epsilon=epsilon, alpha=alpha, n = n_step)
for result in n_step_sarsa_results:
average_reward_n_step_sarsa.append(result[1])
q_var_n_step_sarsa.append(result[4])
all_rewards_per_episode_n_step_sarsa.append(result[3])
print("Done nstep sarsa")
n_step_expected_sarsa_results = Parallel(n_jobs=-2, verbose=10)(delayed(n_step_expected_sarsa)(gw, n, alpha, gamma, epsilon, n=4) for alpha in alphas)
# Q, average_reward, max_reward, all_rewards, Q_variances = n_step_sarsa(gw, max_episode, epsilon=epsilon, alpha=alpha, n = n_step)
for result in n_step_expected_sarsa_results:
average_reward_n_step_expected_sarsa.append(result[1])
q_var_n_step_expected_sarsa.append(result[4])
all_rewards_per_episode_n_step_expected_sarsa.append(result[3])
print("Done nstep expected sarsa")
n_step_tree_backup_results = Parallel(n_jobs=-2, verbose=10)(delayed(n_step_tree_backup)(gw, n, alpha, gamma, epsilon, n=4) for alpha in alphas)
# Q, average_reward, max_reward, all_rewards, Q_variances = n_step_tree_backup(gw, max_episode, epsilon=epsilon, alpha=alpha, n = n_step)
for result in n_step_tree_backup_results:
average_reward_n_step_tree_backup.append(result[1])
q_var_n_step_tree_backup.append(result[4])
all_rewards_per_episode_n_step_tree_backup.append(result[3])
print("Done nstep tree backup")
n_step_q_sigma_results = Parallel(n_jobs=-2, verbose=10)(delayed(n_step_q_sigma)(gw, n, alpha, gamma, epsilon, n=4) for alpha in alphas)
# Q, average_reward, max_reward, all_rewards, Q_variances = n_step_q_sigma(gw, max_episode, epsilon=epsilon, alpha=alpha, n = n_step)
for result in n_step_q_sigma_results:
average_reward_n_step_q_sigma.append(result[1])
q_var_n_step_q_sigma.append(result[4])
all_rewards_per_episode_n_step_q_sigma.append(result[3])
print("Done nstep q_sigma")
q_var_sarsa = np.mean(np.mean(np.split(np.array(q_var_sarsa), number_of_runs), axis = 0), axis=1)
q_var_expected_sarsa = np.mean(np.mean(np.split(np.array(q_var_expected_sarsa), number_of_runs), axis = 0), axis=1)
q_var_double_expected_sarsa = np.mean(np.mean(np.split(np.array(q_var_double_expected_sarsa), number_of_runs), axis = 0), axis=1)
q_var_double_sarsa = np.mean(np.mean(np.split(np.array(q_var_double_sarsa), number_of_runs), axis = 0), axis=1)
q_var_n_step_sarsa = np.mean(np.mean(np.split(np.array(q_var_n_step_sarsa), number_of_runs), axis = 0), axis=1)
q_var_n_step_expected_sarsa = np.mean(np.mean(np.split(np.array(q_var_n_step_expected_sarsa), number_of_runs), axis = 0), axis=1)
q_var_n_step_tree_backup= np.mean(np.mean(np.split(np.array(q_var_n_step_tree_backup), number_of_runs), axis = 0), axis=1)
q_var_n_step_q_sigma= np.mean(np.mean(np.split(np.array(q_var_n_step_q_sigma), number_of_runs), axis = 0), axis=1)
plt.plot(alphas, q_var_double_sarsa, label="Double Sarsa")
plt.plot(alphas, q_var_expected_sarsa, label="Expected Sarsa")
plt.plot(alphas, q_var_double_expected_sarsa, label="Double Expected Sarsa")
plt.plot(alphas, q_var_sarsa, label="Sarsa")
plt.plot(alphas, q_var_n_step_sarsa, label="N-Step Sarsa")
plt.plot(alphas, q_var_n_step_expected_sarsa, label="N-Step Expected Sarsa")
plt.plot(alphas, q_var_n_step_tree_backup, label="N-Step Tree Backup")
plt.plot(alphas, q_var_n_step_q_sigma, label="N-Step Q sigma")
plt.ylabel('Average Q Variance')
plt.xlabel('alpha')
ax = plt.gca()
# ax.set_xscale('symlog')
ax.legend(loc='upper center', shadow=True)
plt.savefig('q_variance.png')
plt.close()
# TODO: plot all sarsa, expected_sarsa, double_Sarsa
# import pdb; pdb.set_trace()
average_reward_double_sarsa = np.mean(np.split(np.array(average_reward_double_sarsa), number_of_runs), axis=0)
average_reward_expected_sarsa = np.mean(np.split(np.array(average_reward_expected_sarsa), number_of_runs), axis=0)
average_reward_double_expected_sarsa = np.mean(np.split(np.array(average_reward_double_expected_sarsa), number_of_runs), axis=0)
average_reward_sarsa = np.mean(np.split(np.array(average_reward_sarsa), number_of_runs), axis=0)
average_reward_n_step_sarsa = np.mean(np.split(np.array(average_reward_n_step_sarsa), number_of_runs), axis=0)
average_reward_n_step_expected_sarsa = np.mean(np.split(np.array(average_reward_n_step_expected_sarsa), number_of_runs), axis=0)
average_reward_n_step_tree_backup = np.mean(np.split(np.array(average_reward_n_step_tree_backup), number_of_runs), axis=0)
average_reward_n_step_q_sigma = np.mean(np.split(np.array(average_reward_n_step_q_sigma), number_of_runs), axis=0)
plt.plot(alphas, average_reward_double_sarsa, label="Double Sarsa")
plt.plot(alphas, average_reward_expected_sarsa, label="Expected Sarsa")
plt.plot(alphas, average_reward_double_expected_sarsa, label="Double Expected Sarsa")
plt.plot(alphas, average_reward_sarsa, label="Sarsa")
plt.plot(alphas, average_reward_n_step_sarsa, label="N-Step Sarsa")
plt.plot(alphas, average_reward_n_step_expected_sarsa, label="N-Step Exepected Sarsa")
plt.plot(alphas, average_reward_n_step_tree_backup, label="N-Step Tree Backup")
plt.plot(alphas, average_reward_n_step_q_sigma, label="N-Step Q Sigma")
plt.ylabel('Average reward')
plt.xlabel('alpha')
ax = plt.gca()
# ax.set_xscale('symlog')
ax.legend(loc='lower center', shadow=True)
plt.savefig('average_reward.png')
plt.close()
# import pdb; pdb.set_trace()
print("Max alpha SARSA: %f" % alphas[np.argmax(average_reward_sarsa)])
print("Max alpha Expected SARSA: %f" % alphas[np.argmax(average_reward_expected_sarsa)])
print("Max alpha Double SARSA: %f" % alphas[np.argmax(average_reward_double_sarsa)])
print("Max alpha Double Expected SARSA: %f" % alphas[np.argmax(average_reward_double_expected_sarsa)])
print("Max alpha N-step SARSA: %f" % alphas[np.argmax(average_reward_n_step_sarsa)])
print("Max alpha N-step Expected SARSA: %f" % alphas[np.argmax(average_reward_n_step_expected_sarsa)])
print("Max alpha N-step TreeBackup: %f" % alphas[np.argmax(average_reward_n_step_tree_backup)])
print("Max alpha N-step Q Sigma: %f" % alphas[np.argmax(average_reward_n_step_q_sigma)])
all_rewards_per_episode_double_sarsa = np.mean(np.split(np.array(all_rewards_per_episode_double_sarsa), number_of_runs), axis=0)
all_rewards_per_episode_expected_sarsa = np.mean(np.split(np.array(all_rewards_per_episode_expected_sarsa), number_of_runs), axis=0)
all_rewards_per_episode_double_expected_sarsa = np.mean(np.split(np.array(all_rewards_per_episode_double_expected_sarsa), number_of_runs), axis=0)
all_rewards_per_episode_sarsa = np.mean(np.split(np.array(all_rewards_per_episode_sarsa), number_of_runs), axis=0)
all_rewards_per_episode_n_step_sarsa = np.mean(np.split(np.array(all_rewards_per_episode_n_step_sarsa), number_of_runs), axis=0)
all_rewards_per_episode_n_step_expected_sarsa = np.mean(np.split(np.array(all_rewards_per_episode_n_step_expected_sarsa), number_of_runs), axis=0)
all_rewards_per_episode_n_step_tree_backup = np.mean(np.split(np.array(all_rewards_per_episode_n_step_tree_backup), number_of_runs), axis=0)
all_rewards_per_episode_n_step_q_sigma= np.mean(np.split(np.array(all_rewards_per_episode_n_step_q_sigma), number_of_runs), axis=0)
variance_double_sarsa = np.var(all_rewards_per_episode_double_sarsa, axis=1)
variance_double_expected_sarsa = np.var(all_rewards_per_episode_double_expected_sarsa, axis=1)
variance_expected_sarsa = np.var(all_rewards_per_episode_expected_sarsa, axis=1)
variance_sarsa = np.var(all_rewards_per_episode_sarsa, axis=1)
variance_n_step_sarsa = np.var(all_rewards_per_episode_n_step_sarsa, axis=1)
variance_n_step_expected_sarsa = np.var(all_rewards_per_episode_n_step_expected_sarsa, axis=1)
variance_n_step_tree_backup = np.var(all_rewards_per_episode_n_step_tree_backup, axis=1)
variance_n_step_q_sigma = np.var(all_rewards_per_episode_n_step_q_sigma, axis=1)
# import pdb; pdb.set_trace()/
plt.plot(alphas, variance_double_sarsa, label="Double Sarsa")
plt.plot(alphas, variance_expected_sarsa, label="Expected Sarsa")
plt.plot(alphas, variance_double_expected_sarsa, label="Double Expected Sarsa")
plt.plot(alphas, variance_sarsa, label="Sarsa")
plt.plot(alphas, variance_n_step_sarsa, label="N-Step Sarsa")
plt.plot(alphas, variance_n_step_expected_sarsa, label="N-Step Expected Sarsa")
plt.plot(alphas, variance_n_step_tree_backup, label="N-Step Tree Backup")
plt.plot(alphas, variance_n_step_q_sigma, label="N-Step Q sigma")
plt.ylabel('Variance in Reward')
plt.xlabel('alpha')
ax = plt.gca()
# ax.set_xscale('symlog')
ax.legend(loc='upper center', shadow=True)
plt.savefig('variance_reward.png')
plt.close()