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» Conjectural Equilibrium in Multiagent Learning
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TSMC
2008
146views more  TSMC 2008»
15 years 1 months ago
Decentralized Learning in Markov Games
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is tha...
Peter Vrancx, Katja Verbeeck, Ann Nowé
ATAL
2006
Springer
15 years 4 months ago
Learning to cooperate in multi-agent social dilemmas
In many Multi-Agent Systems (MAS), agents (even if selfinterested) need to cooperate in order to maximize their own utilities. Most of the multi-agent learning algorithms focus on...
Jose Enrique Munoz de Cote, Alessandro Lazaric, Ma...
ICML
2003
IEEE
16 years 1 months ago
Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining
Learning in many multi-agent settings is inherently repeated play. This calls into question the naive application of single play Nash equilibria in multi-agent learning and sugges...
Jeff L. Stimpson, Michael A. Goodrich
CORR
2011
Springer
230views Education» more  CORR 2011»
14 years 8 months ago
Computational Rationalization: The Inverse Equilibrium Problem
Modeling the behavior of imperfect agents from a small number of observations is a difficult, but important task. In the singleagent decision-theoretic setting, inverse optimal co...
Kevin Waugh, Brian Ziebart, J. Andrew Bagnell
AAAI
1998
15 years 2 months ago
The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems
Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multiagent systems. We examine some of the factors that...
Caroline Claus, Craig Boutilier