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Offline Reinforcement Learning on D4RL v2

60Score (HalfCheetah-M)Diffusion-DICE
43.3647.685256.32Jul 29, 2024

Evaluation results

MethodLinks
Diffusion-DICEPolicy Type=Diffusion policy2024.0760——100.289.349.2102.390.897.3112.2114.198.18291.385.768.67278.367.884.483.8
QGPOPolicy Type=Diffusion policy2024.0754.1——988647.696.984.493.5108110.796.474.483.683.866.664.8——73.954.2
Diffusion-QLPolicy Type=Diffusion policy2024.0751.1——90.58747.8101.395.596.8111.1110.193.466.276.678.646.456.660.562.672.857.3
IDQLPolicy Type=Diffusion policy2024.0751——65.482.545.992.185.195.9108.6112.79480.284.584.863.567.9————
SQLPolicy Type=Gaussian policy2024.0748.3——75.584.244.899.781.294111.811092.27480.279.153.252.3————
IQLPolicy Type=Gaussian policy2024.0747.4——66.372.544.295.276.186.7101.5110.685.566.772.27139.647.546.35171.537.3
O-DICEPolicy Type=Gaussian policy2024.0747.4——86.184.94499.983.693.2110.8110.894.179.58682.755.954————
SfBCPolicy Type=Diffusion policy2024.0745.9——57.177.937.186.265.192.6108.6109.89285.381.38259.345.5————
CQLPolicy Type=Gaussian policy2024.0744——58.572.545.59577.290.7105.4109.684.843.465.25438.431.649.85137.539.2
DTseeds=32021.06—672.6———————————————————
TD3+BCseeds=5, hyperparameter tuning=none, evaluation protocol=average over final 10 evaluations2021.06—677.41,013.2——————————————————