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» Self-evaluated Learning Agent in Multiple State Games
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JMLR
2008
92views more  JMLR 2008»
13 years 7 months ago
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
This paper presents the dynamics of multiple learning agents from an evolutionary game theoretic perspective. We provide replicator dynamics models for cooperative coevolutionary ...
Liviu Panait, Karl Tuyls, Sean Luke
NIPS
2001
13 years 8 months ago
The Steering Approach for Multi-Criteria Reinforcement Learning
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unknown. In addition, the environment may contain arbitrarily varying ele...
Shie Mannor, Nahum Shimkin
ACG
2009
Springer
14 years 2 months ago
Monte-Carlo Tree Search in Settlers of Catan
Abstract. Games are considered important benchmark tasks of artificial intelligence research. Modern strategic board games can typically be played by three or more people, which m...
Istvan Szita, Guillaume Chaslot, Pieter Spronck
SIGECOM
2011
ACM
249views ECommerce» more  SIGECOM 2011»
12 years 10 months ago
Leading dynamics to good behavior
: Many natural games can have a dramatic difference between the quality of their best and worst Nash equilibria, even in pure strategies. Yet, nearly all work to date on dynamics s...
Maria-Florina Balcan
CIG
2005
IEEE
14 years 1 months ago
Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions
AbstractGroup utility functions are an extension of the common team utility function for providing multiple agents with a common reinforcement learning signal for learning cooperat...
Jay Bradley, Gillian Hayes