V-learning : a simple, efficient, decentralized algorithm for multiagent reinforcement learning
Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu
| Year of publication: |
2024
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|---|---|
| Authors: | Jin, Chi ; Liu, Qinghua ; Wang, Yuanhao ; Yu, Tiancheng |
| Published in: |
Mathematics of operations research. - Hanover, Md. : INFORMS, ISSN 1526-5471, ZDB-ID 2004273-5. - Vol. 49.2024, 4, p. 2295-2322
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| Subject: | (coarse) correlated equilibria | decentralized reinforcement learning | Markov games | multiagent reinforcement learning | Nash equilibria | V-learning | Lernprozess | Learning process | Lernen | Learning | Nash-Gleichgewicht | Nash equilibrium | Spieltheorie | Game theory | Agentenbasierte Modellierung | Agent-based modeling | Algorithmus | Algorithm | Dezentralisierung | Decentralization | Nichtkooperatives Spiel | Noncooperative game |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1287/moor.2021.0317 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10015197911