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New framework for online resource allocation using Markov states

By PulseAugur Editorial · [1 sources] ·

This research paper introduces a novel online resource allocation framework that utilizes an endogenous Markov state, where actions influence future state transitions and rewards. The study identifies conditions under which frequent or infrequent re-solving of linear programming (LP) problems leads to optimal or near-optimal outcomes, with a focus on regret minimization. The paper proposes a U-shaped infrequent re-solving policy for scenarios with unknown request priors, demonstrating its effectiveness in coordinating learning and inventory correction. AI

IMPACT Introduces a theoretical framework for optimizing resource allocation in systems with complex state dynamics, potentially impacting AI agent design.

RANK_REASON The item is an academic paper published on arXiv detailing a new theoretical framework and policy for online resource allocation. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

New framework for online resource allocation using Markov states

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The item is an academic paper published on arXiv detailing a new theoretical framework and policy for online resource allocation. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhaohua Chen ·

    Online Resource Allocation with an Endogenous Markov State: Fewer LP Solves Earn More

    arXiv:2610.09577v1 Announce Type: new Abstract: We study finite-horizon online resource allocation with i.i.d. requests and an endogenous Markov state on a finite state space: each action affects the transition of the state that governs future rewards and resource consumption. In…

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