AI RESEARCH
SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning
arXiv CS.LG
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ArXi:2602.03201v3 Announce Type: replace Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of informative gradients in sparse settings, where standard reward models often yield flat landscapes that struggle to guide planning. To address this challenge, we propose Shaping Landscapes with Optimistic Potential Estimates (SLOPE), a novel framework that shifts reward modeling from predicting sparse scalars to constructing informative potential landscapes.