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Multi-Objective Reinforcement Learning on Amsterdam transport planning environment

0.97Normalized Hypervolume

PCN

-0.03880.22310.4850.7469Nov 27, 2024
Updated 23d ago

Evaluation Results

MethodLinks
2024.11
0.970.990.820.13
2024.11
0.810.940.990.01
2024.11
0.80.950.690.48
2024.11
0.80.930.930.03
2024.11
0.790.920.860.06
2024.11
0.670.70.880.37
2024.11
0.530.550.710.45
2024.11
0.410.210.450.43
2024.11
0.270.150.850.4
2024.11
0.220.850.540.64
2024.11
0.170.750.420.58
2024.11
0.120.30.160.54
2024.11
0.060.920.470.62
2024.11
00.670.320.7
2024.11
00.80.430.67
2024.11
00.760.490.63
2024.11
00.620.420.66
2024.11
00.590.340.71
2024.11
00.360.370.72
2024.11
00.320.220.74
2024.11
00.550.610.52
2024.11
00.580.660.47
2024.11
00.470.730.5
2024.11
00.790.450.6
2024.11
00.440.890.67
2024.11
00.50.620.57
2024.11
00.460.360.61
2024.11
00.790.40.64
2024.11
00.860.430.62
2024.11
00.890.410.64
2024.11
00.90.340.69
2024.11
00.880.420.69
2024.11
00.920.380.71
2024.11
00.210.90.4
2024.11
00.170.540.56
2024.11
00.120.470.54
2024.11
00.110.830.5
2024.11
00.120.390.6
2024.11
00.220.90.65
2024.11
00.360.820.58