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» Reinforcement Learning of Local Shape in the Game of Go
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ACG
2003
Springer
14 years 4 days ago
Evaluation in Go by a Neural Network using Soft Segmentation
In this article a neural network architecture is presented that is able to build a soft segmentation of a two-dimensional input. This network architecture is applied to position ev...
Markus Enzenberger
GECCO
2009
Springer
200views Optimization» more  GECCO 2009»
14 years 1 months ago
Apply ant colony optimization to Tetris
Tetris is a falling block game where the player’s objective is to arrange a sequence of different shaped tetrominoes smoothly in order to survive. In the intelligence games, ag...
Xingguo Chen, Hao Wang, Weiwei Wang, Yinghuan Shi,...
ICANN
2010
Springer
13 years 7 months ago
Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients
Abstract. Developing superior artificial board-game players is a widelystudied area of Artificial Intelligence. Among the most challenging games is the Asian game of Go, which, des...
Mandy Grüttner, Frank Sehnke, Tom Schaul, J&u...
CEC
2010
IEEE
13 years 7 months ago
Coevolutionary Temporal Difference Learning for small-board Go
—In this paper we apply Coevolutionary Temporal Difference Learning (CTDL), a hybrid of coevolutionary search and reinforcement learning proposed in our former study, to evolve s...
Krzysztof Krawiec, Marcin Szubert
FLAIRS
2008
13 years 9 months ago
Reinforcement of Local Pattern Cases for Playing Tetris
In the paper, we investigate the use of reinforcement learning in CBR for estimating and managing a legacy case base for playing the game of Tetris. Each case corresponds to a loc...
Houcine Romdhane, Luc Lamontagne