New algorithms tackle reinforcement learning with partial adversarial transitions

By PulseAugur Editorial · [1 sources] · 2026-06-02 04:00

Researchers have developed new algorithms for reinforcement learning in environments with partially adversarial transitions. These algorithms utilize "conditioned occupancy measures" to maintain stability across episodes, even when facing adversarial behavior at specific points. The proposed methods achieve improved regret bounds compared to existing approaches, with one algorithm offering a reduction in regret that removes the need to identify the adversarial steps. AI

IMPACT Introduces novel algorithms for reinforcement learning in complex environments, potentially improving agent performance in scenarios with unpredictable elements.

RANK_REASON This is a research paper detailing new algorithms for a specific machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

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Ofir Schlisselberg

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arXiv cs.LG TIER_1 English(EN) · Ofir Schlisselberg, Tal Lancewicki, Yishay Mansour · 2026-06-02 04:00

Online Learning in MDPs with Partially Adversarial Transitions and Losses

arXiv:2602.09474v2 Announce Type: replace Abstract: We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of $\Lambda$ steps per episode. This model captures environments that are stable except…

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Online Learning in MDPs with Partially Adversarial Transitions and Losses

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