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ATAL
2007
Springer
15 years 10 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»
16 years 1 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
15 years 10 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...
121
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ICASSP
2011
IEEE
14 years 7 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»
14 years 11 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...