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Self-teaching adaptive dynamic programming for Gomoku
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DOI：10.1016/j.neucom.2011.05.032.png)
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In this paper adaptive dynamic programming (ADP) is applied to learn to play Gomoku. The critic network is used to evaluate board situations. The basic idea is to penalize the last move taken by the loser and reward the last move selected by the winner at the end of a game. The results show that the presented program is able to improve its performance by playing against itself and has approached the candidate level of a commercial Gomoku program called 5-star Gomoku. We also examined the influence of two methods for generating games: self-teaching and learning through watching two experts playing against each other and presented the comparison results and reasons. (C) 2011 Elsevier B.V. All rights reserved.
Keyword:
Gomoku
Reinforcement learning
Adaptive dynamic programming
Temporal difference learning
Neural network
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6.5
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2.5W
被引数:
6.5W
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