Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration

2026-03-18

AI Summary

Offline reinforcement learning is inherently limited by its pessimistic nature, which constrains the agent’s ability to explore online and collect new data. To address this, this work proposes a vector field–based reward shaping method that guides non-adaptive policies to safely and persistently explore along the boundary of the uncertainty manifold. The approach integrates an uncertainty oracle, a gradient alignment term, and a rotational flow component to steer exploration while preserving task performance. By introducing vector fields into reward shaping—a novel contribution in this context—the method effectively mitigates degenerate stagnation behaviors commonly observed during boundary exploration. Empirical results in 2D continuous navigation tasks demonstrate that the agent efficiently gathers high-information, safe new data without compromising primary task performance.

Technology Category

• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

While offline reinforcement learning provides reliable policies for real-world deployment, its inherent pessimism severely restricts an agent's ability to explore and collect novel data online. Drawing inspiration from safe reinforcement learning, exploring near the boundary of regions well covered by the offline dataset and reliably modeled by the simulator allows an agent to take manageable risks--venturing into informative but moderate-uncertainty states while remaining close enough to familiar regions for safe recovery. However, naively rewarding this boundary-seeking behavior can lead to a degenerate parking behavior, where the agent simply stops once it reaches the frontier. To solve this, we propose a novel vector-field reward shaping paradigm designed to induce continuous, safe boundary exploration for non-adaptive deployed policies. Operating on an uncertainty oracle trained from offline data, our reward combines two complementary components: a gradient-alignment term that attracts the agent toward a target uncertainty level, and a rotational-flow term that promotes motion along the local tangent plane of the uncertainty manifold. Through theoretical analysis, we show that this reward structure naturally induces sustained exploratory behavior along the boundary while preventing degenerate solutions. Empirically, by integrating our proposed reward shaping with Soft Actor-Critic on a 2D continuous navigation task, we validate that agents successfully traverse uncertainty boundaries while balancing safe, informative data collection with primary task completion.

Problem

• offline reinforcement learning
• safe exploration
• reward shaping
• uncertainty boundary
• degenerate behavior

Innovation

• vector-field reward shaping
• offline reinforcement learning
• safe exploration
• uncertainty manifold
• boundary traversal