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ECML
2004
Springer
14 years 1 months ago
Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics
In this paper, we show how the dynamics of Q-learning can be visualized and analyzed from a perspective of Evolutionary Dynamics (ED). More specifically, we show how ED can be use...
Pieter Jan't Hoen, Karl Tuyls
TSMC
2008
146views more  TSMC 2008»
13 years 7 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
INFSOF
2008
90views more  INFSOF 2008»
13 years 7 months ago
JADE: A software framework for developing multi-agent applications. Lessons learned
Since a number of years agent technology is considered one of the most innovative technologies for the development of distributed software systems. While not yet a mainstream appr...
Fabio Bellifemine, Giovanni Caire, Agostino Poggi,...
ICML
2003
IEEE
14 years 8 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
FLAIRS
2003
13 years 9 months ago
Distributed Knowledge Representation in Neural-Symbolic Learning Systems: A Case Study
Neural-symbolic integration concerns the integration of symbolic and connectionist systems. Distributed knowledge representation is traditionally seen under a purely symbolic pers...
Artur S. d'Avila Garcez, Luís C. Lamb, Krys...