Difference between revisions of "Tomoyuki Kaneko"

From Chessprogramming wiki
Jump to: navigation, search
Line 42: Line 42:
 
* [[Tianhe Wang]], [[Tomoyuki Kaneko]] ('''2018'''). ''Application of Deep Reinforcement Learning in Werewolf Game Agents''. [[TAAI 2018]]
 
* [[Tianhe Wang]], [[Tomoyuki Kaneko]] ('''2018'''). ''Application of Deep Reinforcement Learning in Werewolf Game Agents''. [[TAAI 2018]]
 
* [[Hyunwoo Oh]], [[Tomoyuki Kaneko]] ('''2018'''). ''Deep Recurrent Q-Network with Truncated History''. [[TAAI 2018]]
 
* [[Hyunwoo Oh]], [[Tomoyuki Kaneko]] ('''2018'''). ''Deep Recurrent Q-Network with Truncated History''. [[TAAI 2018]]
 +
* [[Yusaku Mandai]], [[Tomoyuki Kaneko]] ('''2019'''). ''RankNet for evaluation functions of the game of Go''. [[ICGA Journal#41_2|ICGA Journal, Vol. 41, No. 2]]
  
 
=External Links=  
 
=External Links=  

Revision as of 13:36, 14 October 2019

Home * People * Tomoyuki Kaneko

Tomoyuki Kaneko [1]

Tomoyuki Kaneko,
a Japanese computer scientist, and associate professor at Graduate School of the University of Tokyo. His research interests include machine learning in games, and automated feature construction for evaluation functions of general game players. He is co-author of the open source Shogi program GPS Shogi [2], available under GPL version 2 or later. In April 2013, GPS Shogi, running on a computer cluster of 700 PCs in the University of Tokyo, beat Hiroyuki Miura, one of the Top-10 professional Shogi players [3] [4].

See also

Selected Publications

[5] [6]

2000 ...

2005 ...

2010 ...

2015 ...

External Links

References

Up one level