Progressive research on the exploitation-oriented learning XoL

本研究完成了一种可处理多种奖励和惩罚的开发导向学习（XoL）方法。此外，XoL方法的奖励和惩罚的设计准则已通过说明性的例子，即课程分类任务，肌腱驱动的机器人的腰部轨迹学习任务，并在多智能体环境中的Keepaway任务提出。指出XoL在应用于实际问题时优于传统的基于动态规划的强化学习。

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.