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Learning complex game functions is still a difficult task. We apply temporal difference learning (TDL), a well-known variant of the reinforcement learning approach, in combination with n-tuple networks to the game Connect-4. Our agent is trained just by self-play. It is able, for the first time, to consistently beat the optimal-playing Minimax agent (in(More)
Learning board games by self-play has a long tradition in computational intelligence for games. Based on Tesauro's seminal success with TD-Gammon in 1994, many successful agents use temporal difference learning today. But in order to be successful with temporal difference learning on game tasks, often a careful selection of features and a large number of(More)
Systems that learn to play board games are often trained by self-play on the basis of temporal difference (TD) learning. Successful examples include Tesauro's well known TD-Gammon and Lucas' Othello agent. For other board games of moderate complexity like Connect Four, we found in previous work that a successful system requires a very rich initial feature(More)
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