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ATAL
2007
Springer
14 years 1 months ago
Reducing the complexity of multiagent reinforcement learning
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
Andriy Burkov, Brahim Chaib-draa
SODA
2010
ACM
160views Algorithms» more  SODA 2010»
14 years 5 months ago
Solving Simple Stochastic Tail Games
Stochastic games are a natural model for open reactive processes: one player represents the controller and his opponent represents a hostile environment. The evolution of the syste...
Hugo Gimbert, Florian Horn
AI
2007
Springer
14 years 1 months ago
Competition and Coordination in Stochastic Games
Agent competition and coordination are two classical and most important tasks in multiagent systems. In recent years, there was a number of learning algorithms proposed to resolve ...
Andriy Burkov, Abdeslam Boularias, Brahim Chaib-dr...
ICASSP
2011
IEEE
12 years 11 months ago
Social norm and long-run learning in peer-to-peer networks
We start by formulating the resource sharing in peer-to-peer (P2P) networks as a random-matching gift-giving game, where self-interested peers aim at maximizing their own long-ter...
Yu Zhang, Mihaela van der Schaar
CDC
2010
IEEE
105views Control Systems» more  CDC 2010»
13 years 2 months ago
Learning in mean-field oscillator games
This research concerns a noncooperative dynamic game with large number of oscillators. The states are interpreted as the phase angles for a collection of non-homogeneous oscillator...
Huibing Yin, Prashant G. Mehta, Sean P. Meyn, Uday...