Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Practical Issues in Temporal Difference Learning
Machine Learning
Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
A unified analysis of value-function-based reinforcement learning algorithms
Neural Computation
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Poker as Testbed for AI Research
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence
A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
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A minimax version of temporal difference learning (minimax TD-learning) is given, similar to minimax Q-learning. The algorithm is used to train a neural net to play Campaign, a two-player zero-sum game with imperfect information of the Markov game class. Two different evaluation criteria for evaluating game-playing agents are used, and their relation to game theory is shown. Also practical aspects of linear programming and fictitious play used for solving matrix games are discussed.