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ICML
1998
IEEE
15 years 10 months ago
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...
Junling Hu, Michael P. Wellman
SIGECOM
2003
ACM
135views ECommerce» more  SIGECOM 2003»
15 years 2 months ago
Playing large games using simple strategies
We prove the existence of -Nash equilibrium strategies with support logarithmic in the number of pure strategies. We also show that the payoffs to all players in any (exact) Nash...
Richard J. Lipton, Evangelos Markakis, Aranyak Meh...
CORR
2008
Springer
122views Education» more  CORR 2008»
14 years 9 months ago
Strategy Improvement for Concurrent Safety Games
We consider concurrent games played on graphs. At every round of the game, each player simultaneously and independently selects a move; the moves jointly determine the transition ...
Krishnendu Chatterjee, Luca de Alfaro, Thomas A. H...
TON
2008
139views more  TON 2008»
14 years 9 months ago
Stochastic learning solution for distributed discrete power control game in wireless data networks
Distributed power control is an important issue in wireless networks. Recently, noncooperative game theory has been applied to investigate interesting solutions to this problem. Th...
Yiping Xing, Rajarathnam Chandramouli
CSL
2008
Springer
14 years 11 months ago
An Optimal Strategy Improvement Algorithm for Solving Parity and Payoff Games
This paper presents a novel strategy improvement algorithm for parity and payoff games, which is guaranteed to select, in each improvement step, an optimal combination of local str...
Sven Schewe