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ICML
2000
IEEE
16 years 7 months ago
Convergence Problems of General-Sum Multiagent Reinforcement Learning
Stochastic games are a generalization of MDPs to multiple agents, and can be used as a framework for investigating multiagent learning. Hu and Wellman (1998) recently proposed a m...
Michael H. Bowling
IAT
2008
IEEE
16 years 29 days ago
Formalizing Multi-state Learning Dynamics
This paper extends the link between evolutionary game theory and multi-agent reinforcement learning to multistate games. In previous work, we introduced piecewise replicator dynam...
Daniel Hennes, Karl Tuyls, Matthias Rauterberg
AAAI
2010
15 years 8 months ago
Multi-Agent Learning with Policy Prediction
Due to the non-stationary environment, learning in multi-agent systems is a challenging problem. This paper first introduces a new gradient-based learning algorithm, augmenting th...
Chongjie Zhang, Victor R. Lesser
UAI
2000
15 years 7 months ago
Learning to Cooperate via Policy Search
Cooperative games are those in which both agents share the same payoff structure. Valuebased reinforcement-learning algorithms, such as variants of Q-learning, have been applied t...
Leonid Peshkin, Kee-Eung Kim, Nicolas Meuleau, Les...
CDC
2010
IEEE
130views Control Systems» more  CDC 2010»
15 years 1 months ago
Generalized efficiency bounds in distributed resource allocation
Game theory is emerging as a popular tool for distributed control of multiagent systems. In order to take advantage of these game theoretic tools the interactions of the autonomous...
Jason R. Marden, Tim Roughgarden