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Game Theory and Reinforcement Learning formally linked in new research papers

By PulseAugur Editorial · [5 sources] ·

Two new academic papers explore the foundational connections between game theory and reinforcement learning. The first paper, "When Is a Game a Markov Decision Process?", by Eduardo C. Garrido-Merchán, provides formal theorems demonstrating how various game theory concepts, such as Nash equilibrium and Shapley value, directly correspond to structures and operators within reinforcement learning. The second paper, "Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective", focuses on the statistical underpinnings of reinforcement learning, particularly in sample-starved scenarios, and offers a non-asymptotic treatment of tabular Markov decision processes. AI

IMPACT These papers provide a deeper theoretical understanding of reinforcement learning by formally linking it to established game theory concepts, potentially leading to more robust and efficient algorithms.

RANK_REASON Two academic papers published on arXiv presenting theoretical research.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

Game Theory and Reinforcement Learning formally linked in new research papers

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COVERAGE [5]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jonathan Lawry ·

    An Axiomatic Framework for Reinforcement Learning in Open Multi-Agent Systems

    Open multi-agent systems, where only a subset of agents are controllable, are central to real-world domains such as smart grids, yet lack a principled foundation for reinforcement learning algorithm design. We study this setting through n-agent ad hoc teamwork (NAHT), a recent fr…

  2. arXiv cs.LG TIER_1 English(EN) · Eduardo C. Garrido-Merch\'an ·

    When Is a Game a Markov Decision Process? Correspondences Between Game Theory and Reinforcement Learning

    arXiv:2610.41962v1 Announce Type: cross Abstract: Reinforcement learning and game theory describe the same object from two sides. Game theory says what a rational outcome of a strategic interaction is; reinforcement learning says how an agent could reach one through experience. T…

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Ersha Li ·

    From Single-Agent Self-Discovery to Multi-Agent Adaptation: Scaling Laws of Partner Feedback and Convention Rigidity in Meta Reinforcement Learning Algorithms

    A key milestone toward Recursive Self-Improvement (RSI) is replacing hand-crafted optimizers with meta-learned update rules (such as Disco103 and OPEN) that discover how agents learn. Yet when a self-discovered RL model trains with adapting teammates, does co-evolution sustain ca…

  4. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jakob Foerster ·

    Bayesian Offline Multi-Agent Reinforcement Learning with Latent World Models

    Model-based offline multi-agent reinforcement learning (MARL) provides a promising approach to multi-agent decision-making under partial observability, where the global state is unavailable from offline data. However, limited dataset coverage can lead to substantial out-of-distri…

  5. arXiv stat.ML TIER_1 English(EN) · Yuxin Chen, Yuejie Chi, Jianqing Fan, Gen Li, Yuting Wei, Yuling Yan ·

    Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective

    arXiv:2610.41903v1 Announce Type: new Abstract: Reinforcement learning (RL) embodies a foundational paradigm for sequential decision-making in the face of uncertainty. The rapid growth of model complexity in emerging applications has significantly amplified the challenges of achi…

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