Academic

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

arXiv:2603.18326v1 Announce Type: new 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 complementa

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Amirhossein Roknilamouki, Arnob Ghosh, Eylem Ekici, Ness B. Shroff · · 1 min read · 4 views
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arXiv:2603.18326v1 Announce Type: new 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.

Executive Summary

This article proposes a novel vector-field reward shaping paradigm to induce continuous and safe boundary exploration in offline reinforcement learning. By combining a gradient-alignment term and a rotational-flow term, the agent is attracted to a target uncertainty level while promoting motion along the local tangent plane of the uncertainty manifold. Theoretical analysis shows that this reward structure prevents degenerate solutions, and empirical validation demonstrates successful traversal of uncertainty boundaries in a 2D continuous navigation task. This approach has significant implications for safe and efficient exploration in real-world environments, where offline data may be limited and uncertainty is high. The method's potential to balance exploration and exploitation in non-adaptive deployed policies makes it a valuable contribution to the field of reinforcement learning.

Key Points

  • ▸ Proposes a novel vector-field reward shaping paradigm for safe boundary exploration
  • ▸ Combines gradient-alignment and rotational-flow terms for sustained exploratory behavior
  • ▸ Theoretical analysis shows prevention of degenerate solutions
  • ▸ Empirical validation demonstrates successful traversal of uncertainty boundaries in a 2D continuous navigation task

Merits

Strength

Novel and effective approach to safe boundary exploration in offline reinforcement learning

Technical Soundness

Theoretical analysis and empirical validation provide strong evidence for the method's efficacy

Demerits

Limitation

The method's applicability to high-dimensional spaces and complex uncertainty manifolds is unclear

Scalability

The computational cost of training the uncertainty oracle and computing the reward may be high

Expert Commentary

The proposed vector-field reward shaping paradigm is a significant contribution to the field of reinforcement learning, offering a novel and effective approach to safe boundary exploration in offline reinforcement learning. The method's technical soundness is evident from the theoretical analysis and empirical validation. However, its applicability to high-dimensional spaces and complex uncertainty manifolds requires further investigation. Additionally, the computational cost of training the uncertainty oracle and computing the reward may be a limitation in certain scenarios. Nonetheless, this research has the potential to revolutionize the field of reinforcement learning and has significant implications for safe and efficient exploration in real-world environments.

Recommendations

  • ✓ Future research should investigate the application of this method to more complex tasks and higher-dimensional spaces
  • ✓ The development of more efficient algorithms for training the uncertainty oracle and computing the reward would enhance the method's scalability

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