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New RL method achieves near-optimal sample complexity

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for reinforcement learning that significantly improves sample complexity for recursive entropic risk preferences. The paper introduces a refined analysis of model-based risk-sensitive Q-value iteration, achieving near-optimal sample complexity guarantees. This work closes the gap between existing upper and lower bounds for learning in finite discounted Markov decision processes, particularly concerning the risk parameter and effective horizon. AI

IMPACT This research advances theoretical understanding in reinforcement learning, potentially leading to more efficient AI agents in complex decision-making scenarios.

RANK_REASON The cluster contains an academic paper detailing a new theoretical contribution to reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New RL method achieves near-optimal sample complexity

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The cluster contains an academic paper detailing a new theoretical contribution to reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Amirparsa Bahrami, Oliver Mortensen, Mohammad Sadegh Talebi ·

    Near-Optimal Sample Complexity for Recursive Entropic Risk Reinforcement Learning with a Generative Model

    arXiv:2610.06931v1 Announce Type: new Abstract: In this paper, we study the sample complexities of value and policy learning in finite discounted Markov decision processes (MDPs) under recursive entropic risk preferences with risk parameter \(\beta\neq 0\), assuming access to a g…

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