| Budget Amount *help |
¥76,180,000 (Direct Cost: ¥58,600,000、Indirect Cost: ¥17,580,000)
Fiscal Year 2020: ¥14,560,000 (Direct Cost: ¥11,200,000、Indirect Cost: ¥3,360,000)
Fiscal Year 2019: ¥14,560,000 (Direct Cost: ¥11,200,000、Indirect Cost: ¥3,360,000)
Fiscal Year 2018: ¥14,560,000 (Direct Cost: ¥11,200,000、Indirect Cost: ¥3,360,000)
Fiscal Year 2017: ¥14,560,000 (Direct Cost: ¥11,200,000、Indirect Cost: ¥3,360,000)
Fiscal Year 2016: ¥17,940,000 (Direct Cost: ¥13,800,000、Indirect Cost: ¥4,140,000)
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| Outline of Final Research Achievements |
In order to realize the integration of deep learning and symbolic processing, we constructed methods for deep reinforcement learning and studied a world model to acquire the environment and interactions. In the first half of our research, we struggled with the fast pace of the deep learning domain, as our ideas were often published in papers before we could, but in the second half of our research, based on the points raised in the mid-term review, we revised our research theme and were able to lead to many paper results at top international conferences such as ICLR and ICML. Specifically, multimodal deep generative models, or deployment efficient reinforcement learning methods to utilize world models. In the final year of the project, we proposed new models for self-supervised learning of the cerebral cortex, and made significant progress in the integration of brain science and artificial intelligence.
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