-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathplot_stats.py
159 lines (124 loc) · 5.29 KB
/
plot_stats.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
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
random_dir = 'qml-ba/random/results'
dir1 = 'qml-ba/arch1/a2c_5_ppnn/results'
directory = 'paper'
results1 = ['A2C_5_0_05.02.2023_07-00-17.csv',
'A2C_5_1_05.02.2023_11-44-10.csv',
'A2C_5_2_06.02.2023_04-10-44.csv',
'A2C_5_3_06.02.2023_08-47-26.csv',
'A2C_5_4_06.02.2023_13-08-06.csv',
'A2C_5_5_06.02.2023_18-05-59.csv',
'A2C_5_6_07.02.2023_05-37-18.csv',
'A2C_5_7_07.02.2023_12-27-24.csv',
'A2C_5_8_08.02.2023_02-57-47.csv',
'A2C_5_9_08.02.2023_18-51-35.csv']
results2 = ['A2Q_5_0_26.02.2023_07-00-37.csv',
'A2Q_5_1_23.02.2023_10-42-20.csv',
'A2Q_5_2_22.02.2023_02-01-45.csv',
'A2Q_5_3_19.03.2023_07-00-23.csv',
'A2Q_5_4_09.04.2023_07-00-16.csv',
'A2Q_5_5_26.02.2023_07-00-37.csv',
'A2Q_5_6_26.02.2023_07-00-37.csv',
'A2Q_5_7_03.04.2023_11-37-17.csv',
'A2Q_5_8_05.03.2023_07-00-27.csv',
'A2Q_5_9_23.02.2023_02-09-18.csv']
results3 = ['Q2C_5_0_05.03.2023_07-00-28.csv',
'Q2C_5_1_24.02.2023_06-30-34.csv',
'Q2C_5_2_26.02.2023_07-00-42.csv',
'Q2C_5_3_22.02.2023_02-13-33.csv',
'Q2C_5_4_09.04.2023_07-00-21.csv',
'Q2C_5_5_09.04.2023_07-00-21.csv',
'Q2C_5_6_19.03.2023_07-00-20.csv',
'Q2C_5_7_26.02.2023_07-00-42.csv',
'Q2C_5_8_19.03.2023_07-00-20.csv',
'Q2C_5_9_26.02.2023_07-00-42.csv']
results4 = ['Q2Q_5_0_22.02.2023_02-26-13.csv',
'Q2Q_5_1_16.04.2023_07-00-19.csv',
'Q2Q_5_2_16.04.2023_07-00-19.csv',
'Q2Q_5_3_19.03.2023_07-00-33.csv',
'Q2Q_5_4_22.02.2023_18-35-45.csv',
'Q2Q_5_5_22.02.2023_18-35-45.csv',
'Q2Q_5_6_22.02.2023_18-35-44.csv',
'Q2Q_5_7_16.04.2023_07-00-19.csv',
'Q2Q_5_8_09.04.2023_07-00-21.csv',
'Q2Q_5_9_22.02.2023_18-35-47.csv']
random_res = ['random_agent_05.02.2023_07-00-07.csv',
'random_agent_05.02.2023_09-08-02.csv',
'random_agent_05.02.2023_11-36-37.csv',
'random_agent_05.02.2023_14-07-35.csv',
'random_agent_05.02.2023_16-22-37.csv',
'random_agent_05.02.2023_18-29-11.csv',
'random_agent_05.02.2023_20-36-01.csv',
'random_agent_05.02.2023_22-43-12.csv',
'random_agent_06.02.2023_00-50-49.csv',
'random_agent_06.02.2023_02-58-07.csv']
scores1 = []
scores2 = []
scores3 = []
scores4 = []
random_scores = []
# Read the data from each specified file
for file in results1:
df = pd.read_csv(os.path.join(dir1, file), nrows=26000)
scores1.append(df["Episode Score"])
for file in results2:
df = pd.read_csv(os.path.join(directory, file), nrows=26000)
scores2.append(df["Episode Score"])
for file in results3:
df = pd.read_csv(os.path.join(directory, file), nrows=26000)
scores3.append(df["Episode Score"])
for file in results4:
df = pd.read_csv(os.path.join(directory, file), nrows=26000)
scores4.append(df["Episode Score"])
#
for file in random_res:
df = pd.read_csv(os.path.join(random_dir, file), nrows=26000)
random_scores.append(df["Episode Score"])
# Calculate the average and standard deviation of all episode scores
avg_scores1 = np.mean(scores1, axis=0)
std_scores1 = np.std(scores1, axis=0)
avg_scores2 = np.mean(scores2, axis=0)
std_scores2 = np.std(scores2, axis=0)
avg_scores3 = np.mean(scores3, axis=0)
std_scores3 = np.std(scores3, axis=0)
avg_scores4 = np.mean(scores4, axis=0)
std_scores4 = np.std(scores4, axis=0)
random_avg_scores = np.mean(random_scores, axis=0)
random_std_scores = np.std(random_scores, axis=0)
# Convert numpy array to pandas data frame
avg_scores1 = pd.DataFrame(avg_scores1, columns=["Average Score"])
avg_scores2 = pd.DataFrame(avg_scores2, columns=["Average Score"])
avg_scores3 = pd.DataFrame(avg_scores3, columns=["Average Score"])
avg_scores4 = pd.DataFrame(avg_scores4, columns=["Average Score"])
random_avg_scores = pd.DataFrame(random_avg_scores, columns=["Average Score"])
# Smooth the result
avg_scores1["Average Score"] = avg_scores1["Average Score"].rolling(window=150, min_periods=1).mean()
avg_scores2["Average Score"] = avg_scores2["Average Score"].rolling(window=150, min_periods=1).mean()
avg_scores3["Average Score"] = avg_scores3["Average Score"].rolling(window=150, min_periods=1).mean()
avg_scores4["Average Score"] = avg_scores4["Average Score"].rolling(window=150, min_periods=1).mean()
random_avg_scores["Average Score"] = random_avg_scores["Average Score"].rolling(window=2000, min_periods=1).mean()
# Plot the data
plt.plot(avg_scores1, label="A2C")
plt.fill_between(avg_scores1.index, avg_scores1["Average Score"] - std_scores1, avg_scores1["Average Score"] + std_scores1, alpha=0.2)
plt.plot(avg_scores2, label="HA2Q")
plt.fill_between(avg_scores2.index, avg_scores2["Average Score"] - std_scores2, avg_scores2["Average Score"] + std_scores2, alpha=0.2)
plt.plot(avg_scores3, label="HQ2C")
plt.fill_between(avg_scores3.index, avg_scores3["Average Score"] - std_scores3, avg_scores3["Average Score"] + std_scores3, alpha=0.2)
plt.plot(avg_scores4, label="HQ2Q")
plt.fill_between(avg_scores4.index, avg_scores4["Average Score"] - std_scores4, avg_scores4["Average Score"] + std_scores4, alpha=0.2)
plt.plot(random_avg_scores, label="Random Agent")
plt.fill_between(random_avg_scores.index, random_avg_scores["Average Score"] - random_std_scores, random_avg_scores["Average Score"] + random_std_scores, alpha=0.2)
plt.xlabel("Episode")
plt.ylabel("Average Score")
plt.legend(loc="upper left")
plt.grid(linestyle='-', linewidth=0.5, color='silver')
plt.xlim(0, 26000)
plt.ylim(0, 530)
print(scores1)
# Save plot
plot = "A2C_Hybrid_ZYZ_20k_isitthat"
plt.savefig(plot)
plt.show()