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Game-Theoretic Multiagent Reinforcement Learning
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Tremendous advances have been made in multiagent reinforcement learning (MARL). MARL corresponds to the learning problem in a multiagent system in which multiple agents learn simultaneously. It is an interdisciplinary field of study with a long history that includes game theory, machine learning, stochastic control, psychology, and optimization. Despite great successes in MARL, there is a lack of a self-contained overview of the literature that covers game-theoretic foundations of modern MARL methods and summarizes the recent advances. The majority of existing surveys are outdated and do not fully cover the recent developments since 2010. In this work, we provide a monograph on MARL that covers both the fundamentals and the latest developments on the research frontier. The goal of this monograph is to provide a self-contained assessment of the current state-of-the-art MARL techniques from a game-theoretic perspective. We expect this work to serve as a stepping stone for both new researchers who are about to enter this fast-growing field and experts in the field who want to obtain a panoramic view and identify new directions based on recent advances.
Forward citations
Cited by 7 Pith papers
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Trajectory-Aware Retrieval Agents for Temporal Decision- Making
TLM reports large accuracy gains on medical and financial temporal-decision tasks by fitting linear trends to retrieved embeddings, but its monotonicity theorem is circular and its baselines omit plain fine-tuned RAG.
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Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning
COffeE-PSRO combines conservative uncertainty penalties with robust replicator dynamics to extract lower-regret equilibrium profiles from offline multi-agent datasets.
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An Agent-Centric Dynamical Systems Perspective on Multi-Agent Reinforcement Learning
Treating MARL training as coupled stochastic dynamical systems lets Lyapunov exponents, recurrence plots, and fractal dimensions characterize individual-agent stability and sensitivity.
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Policy Gradient with Self-Attention for Model-Free Distributed Nonlinear Multi-Agent Games
A self-attention policy trained with policy gradients learns distributed feedback control for multi-team games without models of dynamics or costs.
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Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning
Adding a no-op 'persist' action to independent Q-learning agents creates a mutualistic shield that lets cooperation survive in a diluted, mobile spatial prisoner's dilemma.
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Homing through Reinforcement Learning
In a 2D Q-learning homing model, mean homing time is reported to be non-monotonic in rotational diffusion with a crossover at D_r≈12, and the learned policy is claimed to beat a stochastic-resetting ABP baseline.
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GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective
A position paper claiming that generative-AI agents that model and predict multi-agent dynamics will replace today's reactive MARL approaches.
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