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New algorithms tackle decentralized multi-player reinforcement learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for decentralized multi-player reinforcement learning in episodic Markov Decision Processes (MDPs) with information asymmetry. The proposed methods, mQ-learning, mQ-learning-intervals, mEXC, and mEXC-Bellman, address scenarios with unobserved actions and independent or common rewards. These algorithms achieve competitive regret bounds compared to centralized learning, particularly for a small number of players or limited action sets. AI

IMPACT Introduces novel algorithms that could advance multi-agent reinforcement learning capabilities.

RANK_REASON Academic paper detailing new algorithms for 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 algorithms tackle decentralized multi-player reinforcement learning

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Academic paper detailing new algorithms for 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) · Larissa Xu, King Bi, William Chang ·

    Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry

    arXiv:2608.12753v1 Announce Type: new Abstract: We study decentralized multi-player reinforcement learning in episodic tabular Markov decision processes (MDPs) under three forms of information asymmetry: (A) unobserved actions with common rewards, (B) observed actions with indepe…

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