| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2012: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2011: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Research Abstract |
This research has completed an Exploitation-oriented Learning (XoL) method that can treat multiple rewards and penalties. Furthermore the design guideline of rewards and penalties on the XoL method has been proposed through illustrative examples, namely, a course classification task, a waist-trajectory learning task for a tendon-driven biped robot, and a Keepaway task in a multi-agent environment. It claim that XoL surpass traditional Reinforcement Learning based on Dynamic Programming in application to real-world problem.
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