@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00190729,
 author = {Kyowoon, Lee and Sol-A, Kim and Jaesik, Choi and Seong-Whan, Lee and Kyowoon, Lee and Sol-A, Kim and Jaesik, Choi and Seong-Whan, Lee},
 book = {WCI2018論文集},
 month = {Jul},
 note = {Recently, deep reinforcement learning have achieved super-human performance in the deterministic games with discrete action spaces, such as Atari games and Go. However, it is still not clear how to utilize deep neural networks for discrete actions in complex games in which a minute change of an action could alter the outcome of the games dramatically. To solve this issue, we incorporates a deep neural network for learning game strategy with a kernel-based Monte Carlo tree search for finding actions from continuous space especially for the game of curling. Without any hand-crafted feature, we train our network in supervised learning manner and then reinforcement learning. Recently, our framework outperforms existing programs equipped with several hand-crafted features for curling and won in the Game AI Tournaments (GAT-2018)., Recently, deep reinforcement learning have achieved super-human performance in the deterministic games with discrete action spaces, such as Atari games and Go. However, it is still not clear how to utilize deep neural networks for discrete actions in complex games in which a minute change of an action could alter the outcome of the games dramatically. To solve this issue, we incorporates a deep neural network for learning game strategy with a kernel-based Monte Carlo tree search for finding actions from continuous space especially for the game of curling. Without any hand-crafted feature, we train our network in supervised learning manner and then reinforcement learning. Recently, our framework outperforms existing programs equipped with several hand-crafted features for curling and won in the Game AI Tournaments (GAT-2018).},
 pages = {5--7},
 publisher = {情報処理学会},
 title = {Deep Reinforcement Learning for the Game of Simulated Curling},
 volume = {2018},
 year = {2018}
}