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ARXIV:2605.04368 · REINFORCEMENT LEARNING · SUBMITTED 07 MAY · 20:33 UTC · FRESHNESS STALE
ARXIV:2605.04368REINFORCEMENT LEARNINGSUBMITTED 07 MAY · 20:33 UTCFRESHNESS STALEKris De Asis · Mohamed Elsayed · Jiamin He · arXiv
A generalization of differential temporal difference methods that extends reinforcement learning to episodic problems while maintaining policy ordering.
Opportunity summary
Pain A generalization of differential temporal difference methods that extends reinforcement learning to episodic problems while maintaining policy ordering.
Evidence 0 refs | 3 sources | 50% coverage
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A generalization of differential temporal difference methods that extends reinforcement learning to episodic problems while maintaining policy ordering. They rely on reward centering, where each reward is centered by the average reward.
Differential temporal difference (TD) methods are value-based reinforcement learning algorithms that have been proposed for infinite-horizon problems. They rely on reward centering, where each reward is centered by the average reward.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We show equivalence with a form of linear TD, thereby inheriting theoretical guarantees that have been shown for those algorithms.
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 4.0/10.
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A generalization of differential temporal difference methods that extends reinforcement learning to episodic problems while maintaining policy ordering.
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10.48550/arXiv.2605.04368A generalization of differential temporal difference methods that extends reinforcement learning to episodic problems while maintaining policy ordering.
Abstract
Differential temporal difference (TD) methods are value-based reinforcement learning algorithms that have been proposed for infinite-horizon problems. They rely on reward centering, where each reward is centered by the average reward. This keeps the return bounded and removes a value function's state-independent offset. However, reward centering can alter the optimal policy in episodic problems, limiting its applicability. Motivated by recent works that emphasize the role of normalization in streaming deep reinforcement learning, we study reward centering in episodic problems and propose a generalization of differential TD. We prove that this generalization maintains the ordering of policies in the presence of termination, and thus extends differential TD to episodic problems. We show equivalence with a form of linear TD, thereby inheriting theoretical guarantees that have been shown for those algorithms. We then extend several streaming reinforcement learning algorithms to their differential counterparts. Across a range of base algorithms and environments, we empirically validate that reward centering can improve sample efficiency in episodic problems.
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PROBLEM
A generalization of differential temporal difference methods that extends reinforcement learning to episodic problems while maintaining policy ordering. They rely on reward centering, where each reward is centered by the average reward.
METHOD
Differential temporal difference (TD) methods are value-based reinforcement learning algorithms that have been proposed for infinite-horizon problems. They rely on reward centering, where each reward is centered by the average reward.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We show equivalence with a form of linear TD, thereby inheriting theoretical guarantees that have been shown for those algorithms.
WHY NOW
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 4.0/10.
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A generalization of differential temporal difference methods that extends reinforcement learning to episodic problems while maintaining policy ordering. They rely on reward centering, where each reward is centered by the average reward.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Differential temporal difference (TD) methods are value-based reinforcement learning algorithms that have been proposed for infinite-horizon problems. They rely on reward centering, where each reward is centered by the average reward.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We show equivalence with a form of linear TD, thereby inheriting theoretical guarantees that have been shown for those algorithms.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A generalization of differential temporal difference methods that extends reinforcement learning to episodic problems while maintaining policy ordering.
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