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New Q-iteration method tackles continuous-action zero-sum Markov games

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for online fitted Q-iteration in continuous-action zero-sum Markov games. This approach utilizes convex-concave neural network function approximators to ensure a pure-strategy saddle point for the minimax problem. The study establishes finite-sample guarantees for non-linear-quadratic games with continuous states and actions, marking a significant advancement in the field. AI

IMPACT Introduces a novel method for solving complex sequential decision-making problems, potentially impacting AI research in adversarial learning and planning.

RANK_REASON Academic paper detailing a new algorithmic approach and theoretical guarantees. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

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

New Q-iteration method tackles continuous-action zero-sum Markov games

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Academic paper detailing a new algorithmic approach and theoretical guarantees. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · David Fridovich-Keil ·

    $Q$ Can Play That Game: Online Fitted $Q$-Iteration for Continuous-Action Zero-Sum Markov Games with Convex-Concave Function Approximation

    Zero-sum Markov games arise in a wide variety of sequential decision-making problems such as adversarial learning and planning against modeled uncertainties. However, prior work on finite-sample guarantees on the learned state-action value function ($Q$-function) for zero-sum Mar…

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