| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Outline of Final Research Achievements |
This research has achieved several progresses about theory and applications of Exploitation-oriented Learning XoL in multi-agent learning. In multi-agent learning, it is important to avoid the concurrent learning problem that occurs when multiple agents learn simultaneously. Firstly, we have proposed a method to avoid the problem. Secondly, we have focused on a positive effect of an indirect reward which is given to the agent that does not receive a reward directly. Especially, we have proposed a method to reduce the perceptual aliasing problem caused by imperfect perception. We have also described the relationship between our previous multi-agent learning theorem and the positive effect. Lastly, we have extended application areas to show the effectiveness of XoL in multi-agent learning through experiments to Keepaway tasks like soccer games. We believe that these results contribute to claim that XoL surpasses traditional reinforcement learning methods in multi-agent learning.
|
