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Offline Reinforcement Learning on D4RL Hopper random (Mean Normalized Score)

62.1Mean Normalized ScoreAdaptive Policy Selection and Fine-Tuning
-1.8614.74531.3547.955Dec 4, 2025Dec 29, 2025Jan 24, 2026Feb 18, 2026Mar 16, 2026Apr 10, 2026May 6, 2026

Evaluation results

MethodLinks
Adaptive Policy Selection and Fine-TuningPhase=Online, Online Interaction Budget=160 K2026.0562.1
FTPhase=Online, Online Interaction Budget=160 K2026.0557.6
VIPOMethod Category=Conservative model-based2025.1233.4
ADMPOMethod Category=Conservative model-based2025.1232.7
LEQMethod Category=Conservative model-based2025.1232.4
MOBILEMethod Category=Conservative model-based2025.1231.9
MOPOMethod Category=Conservative model-based2025.1231.7
CBOPMethod Category=Bayesian-inspired2025.1231.4
APE-VMethod Category=Conservative model-based2025.1231.3
SUMOMethod Category=Conservative model-based2025.1230.8
EDACMethod Category=Model-free2025.1225.3
NEUBAYMethod Category=Ours2025.1224.5
RAMBOMethod Category=Model-free2025.1221.6
MoMoMethod Category=Conservative model-based2025.1218.3
COMBOMethod Category=Model-free2025.1217.9
MAPLEMethod Category=Bayesian-inspired2025.1210.7
MoDAPMethod Category=Bayesian-inspired2025.128.9
CQLMethod Category=Model-free2025.125.3
BestPhase=Offline, Online Interaction Budget=160 K2026.053.8
OEPhase=Offline, Online Interaction Budget=160 K2026.053.8
OPEPhase=Offline, Online Interaction Budget=160 K2026.050.6