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» Learning a Game Strategy Using Pattern-Weights and Self-play
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CG
2002
Springer
13 years 3 months ago
Learning a Game Strategy Using Pattern-Weights and Self-play
Abstract. This paper demonstrates the use of pattern-weights in order to develop a strategy for an automated player of a non-cooperative version of the game of Diplomacy. Diplomacy...
Ari Shapiro, Gil Fuchs, Robert Levinson
ICML
2003
IEEE
14 years 4 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
ICML
2003
IEEE
13 years 9 months ago
The Significance of Temporal-Difference Learning in Self-Play Training TD-Rummy versus EVO-rummy
Reinforcement learning has been used for training game playing agents. The value function for a complex game must be approximated with a continuous function because the number of ...
Clifford Kotnik, Jugal K. Kalita
FLAIRS
2011
12 years 7 months ago
Learning Opponent Strategies through First Order Induction
In a competitive game it is important to identify the opponent’s strategy as quickly and accurately as possible so that an effective response can be staged. In this vain, this p...
Katie Long Genter, Santiago Ontañón,...
ICML
2005
IEEE
14 years 4 months ago
Learning to compete, compromise, and cooperate in repeated general-sum games
Learning algorithms often obtain relatively low average payoffs in repeated general-sum games between other learning agents due to a focus on myopic best-response and one-shot Nas...
Jacob W. Crandall, Michael A. Goodrich