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ARXIV:2603.18326 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.18326REINFORCEMENT LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEAmirhossein Roknilamouki · Arnob Ghosh · Eylem Ekici · Ness B. Shroff · arXiv
A novel reward shaping method for offline reinforcement learning that encourages safe and continuous exploration of new data frontiers.
Opportunity summary
Pain A novel reward shaping method for offline reinforcement learning that encourages safe and continuous exploration of new data frontiers.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A novel reward shaping method for offline reinforcement learning that encourages safe and continuous exploration of new data frontiers. Drawing inspiration from safe reinforcement learning, exploring near the boundary of regions well covered by…
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…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Through theoretical analysis, we show that this reward structure naturally induces sustained exploratory behavior along the boundary while preventing degenerate solutions. Code availability is…
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
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A novel reward shaping method for offline reinforcement learning that encourages safe and continuous exploration of new data frontiers.
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10.48550/arXiv.2603.18326A novel reward shaping method for offline reinforcement learning that encourages safe and continuous exploration of new data frontiers.
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.
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PROBLEM
A novel reward shaping method for offline reinforcement learning that encourages safe and continuous exploration of new data frontiers. Drawing inspiration from safe reinforcement learning, exploring near the boundary of regions well covered by the offline dataset and reliably m...
METHOD
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...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Through theoretical analysis, we show that this reward structure naturally induces sustained exploratory behavior along the boundary while preventing degenerate solutions. Code availability is flagged in...
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A novel reward shaping method for offline reinforcement learning that encourages safe and continuous exploration of new data frontiers. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Through theoretical analysis, we show that this reward structure naturally induces sustained exploratory behavior along the boundary while preventing degenerate solutions. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel reward shaping method for offline reinforcement learning that encourages safe and continuous exploration of new data frontiers.
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Reinforcement Learning
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