| Budget Amount *help |
¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Outline of Final Research Achievements |
After the success of AlphaGo, many researchers are focusing on games to develop technologies in artificial intelligence. In this research project, we have developed new techniques in machine learning (reinforcement learning) and game tree search to improve the performance of AI agents on games. Our research involves parallel and/or distributed computing to speed-up learning, because the learning requires tremendous amount of computing resources in the existing techniques in reinforcement learning.
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