| Budget Amount *help |
¥5,850,000 (Direct Cost: ¥4,500,000、Indirect Cost: ¥1,350,000)
Fiscal Year 2020: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2019: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Outline of Final Research Achievements |
This study extends deep reinforcement learning techniques into imperfect and multi-player games. As AlphaZero demonstrated, AI agents can learn decent strategies in perfect information games by reinforcement learning techniques. However, the learning is still difficult in more complicated situations, e.g., where some information is hidden and/or more than two agents are involved. Therefore, this study focused on learning in imperfect and multi-player games as a representative of challenging tasks.
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