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New research explores Bellman optimality for average-reward robust Markov Decision Processes

By PulseAugur Editorial · [1 sources] ·

This paper delves into the theoretical underpinnings of average-reward robust Markov Decision Processes (MDPs), a complex area of sequential decision-making. It focuses on the constant-gain setting to establish a general framework, analyzing the robust Bellman equation and its connection to optimal average rewards. The research identifies conditions under which solutions to this equation accurately represent optimal average rewards and stationary policies, thereby advancing dynamic programming theory for long-run average criteria in operational environments. AI

IMPACT Expands dynamic programming theory for robust decision-making under long-run average criteria.

RANK_REASON Academic paper published on arXiv detailing theoretical advancements in Markov Decision Processes. [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 research explores Bellman optimality for average-reward robust Markov Decision Processes

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Academic paper published on arXiv detailing theoretical advancements in Markov Decision Processes. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shengbo Wang, Nian Si ·

    Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain

    arXiv:2509.14203v4 Announce Type: replace-cross Abstract: Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finite-horizon or discounted models. Long-run a…

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