HPML projects multi-agent update fields onto the closest metric-gradient potential flow via Hodge decomposition, yielding Lyapunov potentials and equilibrium-gap bounds.
Markov games as a framework for multi-agent reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Auction-based bidding among selfish local policies in a general-sum Markov game enables dynamic adaptation to evolving multi-objectives in reinforcement learning with Nash equilibrium guarantees.
citing papers explorer
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Metric-Gradient Projection for Stable Multi-Agent Policy Learning
HPML projects multi-agent update fields onto the closest metric-gradient potential flow via Hodge decomposition, yielding Lyapunov potentials and equilibrium-gap bounds.
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Auction-Based Online Policy Adaptation for Evolving Objectives
Auction-based bidding among selfish local policies in a general-sum Markov game enables dynamic adaptation to evolving multi-objectives in reinforcement learning with Nash equilibrium guarantees.