From Scalar Rewards to Potential Trends: Shaping Potential Landscapes for Model-Based Reinforcement Learning

2026-02-03

AI Summary

In sparse-reward environments, model-based reinforcement learning often suffers from limited performance due to the absence of informative gradients and planning guidance when regressing scalar rewards. This work proposes SLOPE, a novel framework that reimagines reward modeling by constructing a potential landscape endowed with exploration-inducing properties, rather than predicting scalar values. By integrating optimistic distributional regression to estimate high-confidence upper bounds, SLOPE amplifies rare success signals and provides meaningful exploration gradients. This approach uniquely unifies potential-based shaping with optimistic distributional estimation, transcending the conventional scalar reward paradigm. Evaluated across five benchmarks encompassing over 30 tasks under fully sparse, semi-sparse, and dense reward settings, SLOPE consistently outperforms state-of-the-art methods.

Technology Category

• Machine Learning: Reinforcement Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Model-based reinforcement learning (MBRL) achieves high sample efficiency by simulating future trajectories with learned dynamics and reward models. However, its effectiveness is severely compromised in sparse reward settings. The core limitation lies in the standard paradigm of regressing ground-truth scalar rewards: in sparse environments, this yields a flat, gradient-free landscape that fails to provide directional guidance for planning. To address this challenge, we propose Shaping Landscapes with Optimistic Potential Estimates (SLOPE), a novel framework that shifts reward modeling from predicting 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 demonstrate that SLOPE consistently outperforms leading baselines in fully sparse, semi-sparse, and dense rewards.

Problem

• sparse rewards
• model-based reinforcement learning
• reward shaping
• potential landscapes
• sample efficiency

Innovation

• potential landscapes
• optimistic distributional regression
• sparse reward
• model-based reinforcement learning
• reward shaping