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ARXIV:2603.09344 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09344REINFORCEMENT LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Robust Regularized Policy Iteration enhances offline reinforcement learning by optimizing policies against worst-case dynamics under transition uncertainty.
Opportunity summary
Pain Robust Regularized Policy Iteration enhances offline reinforcement learning by optimizing policies against worst-case dynamics under transition uncertainty.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Robust Regularized Policy Iteration enhances offline reinforcement learning by optimizing policies against worst-case dynamics under transition uncertainty. The learned policy may visit out-of-distribution state-action pairs where value estimates and learned dynamics are unreliable.
Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift. The learned policy may visit out-of-distribution state-action pairs where value estimates and learned…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift.
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Robust Regularized Policy Iteration enhances offline reinforcement learning by optimizing policies against worst-case dynamics under transition uncertainty.
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10.48550/arXiv.2603.09344Robust Regularized Policy Iteration enhances offline reinforcement learning by optimizing policies against worst-case dynamics under transition uncertainty.
Abstract
Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift. The learned policy may visit out-of-distribution state-action pairs where value estimates and learned dynamics are unreliable. To address policy-induced extrapolation and transition uncertainty in a unified framework, we formulate offline RL as robust policy optimization, treating the transition kernel as a decision variable within an uncertainty set and optimizing the policy against the worst-case dynamics. We propose Robust Regularized Policy Iteration (RRPI), which replaces the intractable max-min bilevel objective with a tractable KL-regularized surrogate and derives an efficient policy iteration procedure based on a robust regularized Bellman operator. We provide theoretical guarantees by showing that the proposed operator is a $γ$-contraction and that iteratively updating the surrogate yields monotonic improvement of the original robust objective with convergence. Experiments on D4RL benchmarks demonstrate that RRPI achieves strong average performance, outperforming recent baselines including percentile-based methods such as PMDB on the majority of environments while remaining competitive on the rest. Moreover, RRPI exhibits robust behavior. The learned $Q$-values decrease in regions with higher epistemic uncertainty, suggesting that the resulting policy avoids unreliable out-of-distribution actions under transition uncertainty.
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PROBLEM
Robust Regularized Policy Iteration enhances offline reinforcement learning by optimizing policies against worst-case dynamics under transition uncertainty. The learned policy may visit out-of-distribution state-action pairs where value estimates and learned dynamics are unrelia...
METHOD
Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift. The learned policy may visit out-of-distribution state-action pairs where value estimates and learned dyna...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift.
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Robust Regularized Policy Iteration enhances offline reinforcement learning by optimizing policies against worst-case dynamics under transition uncertainty. The learned policy may visit out-of-distribution state-action pairs where value estimates and learned dynamics are unreliable.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift. The learned policy may visit out-of-distribution state-action pairs where value estimates and learned dynamics are unreliable.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift.
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 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Robust Regularized Policy Iteration enhances offline reinforcement learning by optimizing policies against worst-case dynamics under transition uncertainty.
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Reinforcement Learning
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