




















Abstract: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. SLOPE employs optimistic distributional regression to estimate high-confidence upper bounds, which amplifies rare success signals and ensures sufficient exploration gradients. Evaluations on 30+ tasks across 5 benchmarks and real-world robotic deployments, demonstrate that SLOPE consistently outperforms leading baselines in fully sparse, semi-sparse, and dense rewards.
| Comments: | Work in progress |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.03201 [cs.LG] |
| (or arXiv:2602.03201v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.03201 arXiv-issued DOI via DataCite |
From: Yao-Hui Li [view email]
[v1]
Tue, 3 Feb 2026 07:13:26 UTC (11,767 KB)
[v2]
Tue, 10 Feb 2026 07:16:29 UTC (11,767 KB)
[v3]
Fri, 8 May 2026 05:37:03 UTC (11,763 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。