| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2014: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Outline of Final Research Achievements |
Learning action strategies from computer simulations has a potential to achieving the drastic productivity increases because it has no necessity to perform an expensive process, i.e., real experiments for collecting the data. However, the behavior of simulations often differ from that of actual environments; thus, it is not rare that the action strategy obtained from the simulation makes no sense in practical applications. In this project, we developed a new framework, so-called data assimilation reinforcement learning (DARL) which incorporates data assimilation and reinforcement learning. DARL can provide the good action strategy in the small number of experiments by learning not only the action strategy but also the computer simulation simultaneously. We have also applied DARL to material design and drug discovery problems and confirmed its effectiveness compared with current methods.
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