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New research proposes Adaptive Batching Policies for reinforcement learning

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces Adaptive Batching Policies (ABPs) for reinforcement learning problems that involve multi-step lookahead information. The authors, including Nadav Merlis, highlight the limitations of existing heuristics like fixed batching and model predictive control. Their proposed ABP approach utilizes lookahead information in state-dependent batches and includes an optimistic regret-minimizing algorithm for learning optimal ABPs in unknown environments, achieving order-optimal regret bounds. AI

IMPACT Introduces a novel approach to reinforcement learning that could improve efficiency and performance in complex decision-making scenarios.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research proposes Adaptive Batching Policies for reinforcement learning

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The cluster contains a research paper published on arXiv detailing a new methodology for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Nadav Merlis ·

    Reinforcement Learning with Multi-Step Lookahead Information Via Adaptive Batching

    arXiv:2601.10418v2 Announce Type: replace-cross Abstract: We study tabular finite-horizon reinforcement learning problems with multiple steps of lookahead information. Before acting, the learner observes $\ell$ steps of future transition and reward realizations: the exact state t…

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