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Offline Reinforcement Learning on D4RL halfcheetah-medium-expert

110Normalized ScoreVIPO
88.05693.75399.45105.147Jun 16, 2021Apr 13, 2022Feb 8, 2023Dec 6, 2023Oct 2, 2024Jul 30, 2025May 27, 2026

Evaluation results

MethodLinks
VIPOMethod Category=Conservative model-based2025.12110—
PSPOAveraged across=4 random seeds2026.05109.7—
NEUBAYMethod Category=Ours2025.12109.5—
PMDB2022.10108.5—
PMDBN=10, k=22022.10108.5—
PMDBNumber of random seeds=42026.03108.5—
PMDBMethod Type=Model-based2023.10108.5—
PMDB2026.05108.5—
MOBILEImplementation=Original Paper2024.11108.2—
MOBILEMethod Category=Conservative model-based2025.12108.2—
MoMoMethod Category=Conservative model-based2025.12107.9—
CABI+TD3-BCMethod Type=Model-based2023.10107.6—
SUMOMethod Category=Conservative model-based2025.12106.6—
EDAC2022.10106.3—
EDACMethod Category=Model-free2025.12106.3—
EDACMethod Type=Model-free2023.10106.3—
FQL2026.05106.1—
MBOP2026.03105.9—
CBOPMethod Category=Bayesian-inspired2025.12105.4—
RRPINumber of random seeds=42026.03105.3—
CROPMethod Type=Model-based2023.10104.9—
MOPOImplementation=Reproduced2024.11104.4—
ADMPOMethod Category=Conservative model-based2025.12103.7—
AMGNumber of random seeds=42026.03103.7—
ADM2026.05103.7—
MoDAPMethod Category=Bayesian-inspired2025.12103.4—
MOBILEImplementation=Reproduced2024.11103.2—
LEQMethod Category=Conservative model-based2025.12102.8—
APE-VMethod Category=Conservative model-based2025.12101.4—
Count-MORLMethod Type=Model-based2023.10100—
ProposedSeeds=52023.1098.5—
MPDiffuser+Rankranking=true2025.1298.4—
SPAR2026.0597—
MPDiffuserranking=false2025.1296.9—
C-LAPImplementation=Reproduced2024.1196.8—
DQLNFE=52026.0396.8—
PLASImplementation=Original Paper2024.1196.6—
TD3+BCSeeds=52023.1095.9—
Decision Stacks2023.0695.7—
Planner2025.1295.7—
EPQNumber of random seeds=42026.0395.7—
EPQ2026.0595.7—
MOREL2021.1095.6—
RAMBONumber of random seeds=42026.0395.4—
RAMBO2026.0595.4—
CQL2022.1095—
CQL2022.1095—
TT2023.0695—
CAMPAccess=Scripted preferences2024.0495—
CQLMethod Category=Model-free2025.1295—
TT2023.0695—
CQLNumber of random seeds=42026.0395—
TT2026.0395—
CQL2026.0595—
ATACSeeds=52023.1094.8—
ATACMethod Type=Model-free2023.1094.8—
IQLAccess=True reward functions2024.0494.7—
IDQL2025.1294.4—
QIPO2026.0594—
RAMBOMethod Category=Model-free2025.1293.7—
RAMBOMethod Type=Model-based2023.1093.7—
Rev. KL Reg.Algorithmic Template=One-step2021.0693.5—
DMambasource=Lv et al., 20242023.0693.5—
DCModel Category=SSM-based2026.0293.1—
DS4Model Category=SSM-based2026.0292.7—
BAIL2021.1092.6—
DMMModel Category=SSM-based2026.0292.6—
KDPNFE=12026.0392.5—
DMambasource=Ota, 20242023.0691.9—
TAP2023.0691.8—
Exp. WeightAlgorithmic Template=One-step2021.0691.7—
CQL2023.0691.6—
CQLModel Category=Value-based2026.0291.6—
CQL2023.0691.6—
CQL2026.0391.6—
DDImplementation=reproduced2023.0691.5—
OptiDICESeeds=52023.1091.1—
DMGNumber of random seeds=42026.0391.1—
DMG2026.0591.1—
BCQ2021.1091—
CQL2021.1090.8—
MOPOMethod Category=Conservative model-based2025.1290.8—
TD3+BCModel Category=Value-based2026.0290.7—
DDImplementation=Original2023.0690.6—
DDCategory=Diffusion Planner2024.0590.6—
Decision Diffuser2025.1290.6—
DMModel Category=SSM-based2026.0290.6—
OPRLAccess=Scripted preferences2024.0490.5—
COMBOSeeds=52023.1090—
COMBO2021.1090—
COMBOCategory=Model-based RL2024.0590—
COMBOMethod Category=Model-free2025.1290—
COMBO2025.1290—
COMBOMethod Type=Model-based2023.1090—
EDTModel Category=Transformer-based2026.0289.7—
OPPOAccess=Scripted preferences, Implementation=Re-implementation2024.0489.6—
AdaptDiffuserCategory=Diffusion Planner2024.0589.6—
ROMI-BCQ2021.1089.5—
OPPOAccess=Scripted preferences, Implementation=Reported2024.0488.9—
DiffuserCategory=Diffusion Planner2024.0588.9—