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» AWESOME: A General Multiagent Learning Algorithm that Conver...
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ICML
2003
IEEE
14 years 5 months ago
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Oppon
A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algori...
Vincent Conitzer, Tuomas Sandholm
ATAL
2004
Springer
13 years 10 months ago
Best-Response Multiagent Learning in Non-Stationary Environments
This paper investigates a relatively new direction in Multiagent Reinforcement Learning. Most multiagent learning techniques focus on Nash equilibria as elements of both the learn...
Michael Weinberg, Jeffrey S. Rosenschein
LAMAS
2005
Springer
13 years 10 months ago
Unifying Convergence and No-Regret in Multiagent Learning
We present a new multiagent learning algorithm, RVσ(t), that builds on an earlier version, ReDVaLeR . ReDVaLeR could guarantee (a) convergence to best response against stationary ...
Bikramjit Banerjee, Jing Peng
AAMAS
2007
Springer
13 years 5 months ago
Generalized multiagent learning with performance bound
Abstract – Despite increasing deployment of agent technologies in several business and industry domains, user confidence in fully automated agent driven applications is noticeab...
Bikramjit Banerjee, Jing Peng
AAAI
2004
13 years 6 months ago
Performance Bounded Reinforcement Learning in Strategic Interactions
Despite increasing deployment of agent technologies in several business and industry domains, user confidence in fully automated agent driven applications is noticeably lacking. T...
Bikramjit Banerjee, Jing Peng