The paper establishes formal theorems linking game theory and reinforcement learning, showing that a game can be viewed as a Markov decision process in three distinct ways—definitional identity, exact reduction, or structural analogy. It systematically maps every standard game class and solution concept to corresponding reinforcement learning models, training procedures, and complexity results. The work also identifies where these correspondences break down, such as non‑stationarity, impossibility of uncoupled convergence to Nash equilibrium, and gaps between coarse correlated and correlated equilibria.
arXiv:2610.03621v1 Announce Type: cross Abstract: There has been a surge of recent work on correlated equilibrium concepts in Markov games. However, existing results focus on concepts weaker than nor...
arXiv:2608. 07301v1 Announce Type: new Abstract: We study what can be recovered about the transition probabilities of a Markov decision process from optimal actions alone.
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
arXiv:2609.22757v1 Announce Type: cross Abstract: A (coarse) correlated equilibrium (CE) is information-value-free (IVF) if a player can match the payoff obtained from recommendations by committing t...
arXiv:2611.07589v1 Announce Type: cross Abstract: In many modern multi-agent systems, a mediator seeks to influence strategic decision dynamics using only outcome-level feedback and without knowing a...
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 independent rewards, and (C) unobserved actions with independent rewards.
arXiv:2607. 17316v1 Announce Type: new Abstract: The softmax policy $\pi(a \mid s) \propto \exp(\beta Q(s,a))$ is the default model of stochastic choice in reinforcement learning (RL).
arXiv:2409. 14557v4 Announce Type: replace-cross Abstract: We study a structured class of Markov Decision Processes, known as Exo-MDPs, in which the state space is partitioned into exogenous and endogenous components.
The monograph "Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective" surveys recent advances in finite-sample statistical theory for reinforcement learning. It focuses on tabular Markov decision processes and examines three sampling settings—generative model, online, and offline RL—alongside model-based and model-free algorithmic paradigms. The work emphasizes minimax-optimal sample complexity, provides self-contained analyses, and briefly explores extensions such as robust, multi-agent, federated, and human-feedback RL.
arXiv:2606. 06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play.
The paper establishes a game-theoretic framework for bisimulation metrics in undiscounted reinforcement learning settings, including total- and average-reward problems. By interpreting metric operators as Bellman–Shapley operators of zero-sum turn-based games, it defines unique metrics even when the discount factor equals one. The authors also explore how metric finiteness relates to MDP structure, introduce a flexible action-space modification framework, and provide bounds linking these metrics to optimal value differences and aggregation-based planning guarantees.
arXiv:2511. 19849v2 Announce Type: replace-cross Abstract: Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $\omega$-regular and linear temporal logic (LTL) objectives.