Multi-Objective Reinforcement Learning on Xi’an transport planning environment benchmark leaderboard · SOTA2 Research

Multi-Objective Reinforcement Learning on Xi’an transport planning environment

0.93Normalized HypervolumePCN

Evaluation Results

Method	Links
PCN2024.11		0.93	0.97	0.78	0.18
PCN2024.11		0.89	0.96	0.87	0.1
LCN2024.11		0.86	0.95	0.93	0.06
PCN2024.11		0.83	0.94	0.67	0.3
LCN-Redist.2024.11		0.79	0.92	0.97	0.01
LCN-Mean2024.11		0.78	0.91	0.9	0.07
LCN2024.11		0.71	0.89	0.91	0.09
LCN2024.11		0.71	0.91	0.91	0.25
LCN-Mean2024.11		0.7	0.89	0.92	0.1
LCN-Redist.2024.11		0.69	0.88	0.96	0.05
LCN2024.11		0.67	0.8	0.87	0.23
LCN-Redist.2024.11		0.64	0.83	0.77	0.21
PCN2024.11		0.63	0.86	0.71	0.3
LCN2024.11		0.6	0.74	0.77	0.33
LCN-Redist.2024.11		0.6	0.89	0.77	0.23
PCN2024.11		0.56	0.83	0.64	0.3
LCN2024.11		0.55	0.5	0.79	0.25
LCN2024.11		0.54	0.79	0.86	0.19
LCN-Redist.2024.11		0.53	0.63	0.59	0.33
LCN-Mean2024.11		0.52	0.78	0.87	0.24
LCN-Mean2024.11		0.51	0.84	0.9	0.25
LCN-Mean2024.11		0.49	0.71	0.84	0.23
LCN-Redist.2024.11		0.48	0.73	0.82	0.18
LCN2024.11		0.46	0.38	0.81	0.3
LCN-Redist.2024.11		0.39	0.52	0.62	0.27
LCN-Mean2024.11		0.37	0.26	0.78	0.26
LCN-Redist.2024.11		0.31	0.86	0.54	0.4
LCN2024.11		0.29	0.42	0.62	0.3
LCN-Mean2024.11		0.28	0.22	0.8	0.32
LCN-Redist.2024.11		0.19	0.92	0.56	0.45
PCN2024.11		0.17	0.57	0.51	0.34
LCN-Mean2024.11		0.15	0.25	0.76	0.3
LCN-Mean2024.11		0.12	0.39	0.71	0.39
GPI-LS2024.11		0.11	0.27	0.21	0.3
GPI-LS2024.11		0.08	0.24	0.26	0.3
PCN2024.11		0.07	0.55	0.46	0.44
GPI-LS2024.11		0.05	0.33	0.23	0.46
GPI-LS2024.11		0.03	0.42	0.25	0.5
GPI-LS2024.11		0.02	0.38	0.2	0.49
PCN2024.11		0.02	0.39	0.45	0.43
PCN2024.11		0	0.27	0.43	0.46