Copertina

Networked Communication for Mean-Field Games with Function Approximation and Empirical Mean-Field Estimation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Benjamin, Patrick, Abate, Alessandro
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Multiagent Systems Artificial Intelligence Computer Science and Game Theory Machine Learning Systems and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Networked Communication for Decentralised Agents in Mean-Field Games
di: Benjamin, Patrick, et al.
Pubblicazione: (2023)

Bounded Rationality Equilibrium Learning in Mean Field Games
di: Eich, Yannick, et al.
Pubblicazione: (2024)

Stochastic Semi-Gradient Descent for Learning Mean Field Games with Population-Aware Function Approximation
di: Zhang, Chenyu, et al.
Pubblicazione: (2024)

MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games
di: Hu, Anran, et al.
Pubblicazione: (2024)

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
di: Wu, Zida, et al.
Pubblicazione: (2024)

Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
di: Fabian, Christian, et al.
Pubblicazione: (2024)

A Single Online Agent Can Efficiently Learn Mean Field Games
di: Zhang, Chenyu, et al.
Pubblicazione: (2024)

Learning Mean Field Control on Sparse Graphs
di: Fabian, Christian, et al.
Pubblicazione: (2025)

Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective
di: Zaman, Muhammad Aneeq uz, et al.
Pubblicazione: (2024)

Scalable Neural Incentive Design with Parameterized Mean-Field Approximation
di: Corecco, Nathan, et al.
Pubblicazione: (2025)

Efficient and Scalable Deep Reinforcement Learning for Mean Field Control Games
di: Peng, Nianli, et al.
Pubblicazione: (2024)

Policy Optimization and Multi-agent Reinforcement Learning for Mean-variance Team Stochastic Games
di: Hu, Junkai, et al.
Pubblicazione: (2025)

When is Mean-Field Reinforcement Learning Tractable and Relevant?
di: Yardim, Batuhan, et al.
Pubblicazione: (2024)

Robust Mean-Field Games with Risk Aversion and Bounded Rationality
di: Jeloka, Bhavini, et al.
Pubblicazione: (2026)

Differentiable Bilevel Programming for Stackelberg Congestion Games
di: Li, Jiayang, et al.
Pubblicazione: (2022)

Optimal Strategy Revision in Population Games: A Mean Field Game Theory Perspective
di: Barreiro-Gomez, Julian, et al.
Pubblicazione: (2025)

Aggregate Fictitious Play for Learning in Anonymous Polymatrix Games (Extended Version)
di: Kara, Semih, et al.
Pubblicazione: (2025)

Choose Your Battles: Distributed Learning Over Multiple Tug of War Games
di: Chandak, Siddharth, et al.
Pubblicazione: (2025)

Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games
di: Lanier, JB, et al.
Pubblicazione: (2026)

Evaluating LLMs in Open-Source Games
di: Sistla, Swadesh, et al.
Pubblicazione: (2025)

Observation Interference in Partially Observable Assistance Games
di: Emmons, Scott, et al.
Pubblicazione: (2024)

Offline Fictitious Self-Play for Competitive Games
di: Chen, Jingxiao, et al.
Pubblicazione: (2024)

Learning Optimal Tax Design in Nonatomic Congestion Games
di: Cui, Qiwen, et al.
Pubblicazione: (2024)

Approximate State Abstraction for Markov Games
di: Ishibashi, Hiroki, et al.
Pubblicazione: (2024)

Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory
di: Çelikok, Mustafa Mert, et al.
Pubblicazione: (2024)

Online Learning of Counter Categories and Ratings in PvP Games
di: Lin, Chiu-Chou, et al.
Pubblicazione: (2025)

RuleSmith: Multi-Agent LLMs for Automated Game Balancing
di: Zeng, Ziyao, et al.
Pubblicazione: (2026)

Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions
di: Deng, Xiaotie, et al.
Pubblicazione: (2021)

Auto-Encoding Bayesian Inverse Games
di: Liu, Xinjie, et al.
Pubblicazione: (2024)

ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
di: Lei, Shiqi, et al.
Pubblicazione: (2024)

ApproxED: Approximate exploitability descent via learned best responses
di: Martin, Carlos, et al.
Pubblicazione: (2023)

Policy Optimization finds Nash Equilibrium in Regularized General-Sum LQ Games
di: Zaman, Muhammad Aneeq uz, et al.
Pubblicazione: (2024)

Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
di: Pásztor, Barna, et al.
Pubblicazione: (2025)

Continuous-time Discounted Mirror-Descent Dynamics in Monotone Concave Games
di: Gao, Bolin, et al.
Pubblicazione: (2019)

Socially-Weighted Alignment: A Game-Theoretic Framework for Multi-Agent LLM Systems
di: Mumcu, Furkan, et al.
Pubblicazione: (2026)

Cooperative Game-Theoretic Credit Assignment for Multi-Agent Policy Gradients via the Core
di: Ji, Mengda, et al.
Pubblicazione: (2025)

Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems
di: Yuwono, Steve, et al.
Pubblicazione: (2024)

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
di: Li, Zun, et al.
Pubblicazione: (2023)

Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics
di: Huynh, Trung-Kiet, et al.
Pubblicazione: (2025)

Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning
di: Kudo, Mikoto, et al.
Pubblicazione: (2026)