Perspectives on multiagent learning

DOI10.1016/j.artint.2007.02.004zbMath1168.68492OpenAlexW1996762597MaRDI QIDQ1028921

Tuomas W. Sandholm

Publication date: 9 July 2009

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2007.02.004

zbMATH Keywords

game theoryreinforcement learningmultiagent learninglearning in games

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Rationality and learning in game theory (91A26)

Related Items (4)

Continuous learning methods in two-buyer pricing problem ⋮ Learning in nonlinear pricing with unknown utility functions ⋮ An adjustment scheme for nonlinear pricing problem with two buyers ⋮ A Survey of Opponent Modeling in Adversarial Domains

Uses Software

• R-MAX
• AWESOME

Cites Work

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• AWESOME
• Consistency and cautious fictitious play
• Efficient learning equilibrium
• New complexity results about Nash equilibria
• Simple search methods for finding a Nash equilibrium
• A note on strategy elimination in bimatrix games
• Nash and correlated equilibria: Some complexity considerations
• Automaton theory and modelling of biological systems. Translated by Scitran (Scientific Translation Service)
• The weighted majority algorithm
• A near-optimal polynomial time algorithm for learning in certain classes of stochastic games
• Multiagent learning using a variable learning rate
• Adaptive game playing using multiplicative weights
• AWESOME: a general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents
• Bayesian learning in repeated games of incomplete information
• Complexity of constructing solutions in the core based on synergies among coalitions
• Efficient algorithms for online decision problems
• 10.1162/153244303765208377
• The Complexity of Eliminating Dominated Strategies
• Rational Learning Leads to Nash Equilibrium
• Lossless abstraction of imperfect information games
• Algorithms for Closed Under Rational Behavior (CURB) Sets
• Prediction, Optimization, and Learning in Repeated Games
• A Simple Adaptive Procedure Leading to Correlated Equilibrium
• Learning Theory
• The Nonstochastic Multiarmed Bandit Problem
• 10.1162/1532443041827880
• Routing without regret
• Learning Theory and Kernel Machines
• Equilibrium Points of Bimatrix Games
• The Empirical Bayes Envelope and Regret Minimization in Competitive Markov Decision Processes

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