Introduction of Fixed Mode States into Online Profit Sharing and Its Application to Waist Trajectory Generation of Biped Robot
Introduction of Fixed Mode States into Online Profit Sharing and Its Application to Waist Trajectory Generation of Biped Robot
复制标题
在线利润共享中引入固定模式状态及其在双足机器人腰部轨迹生成中的应用
DOI：
10.1007/978-3-642-29946-9_29
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Kazuteru Miyazaki and Hiroaki Kobayashi
中科院分区：
文献类型：
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作者：
Seiya Kuroda;Kazuteru Miyazaki and Hiroaki Kobayashi
在长期任务的强化学习中，当智能体的概率动作在任务学习达到目标之前导致太多错误时，学习效率可能会恶化。我们提出的新类型的状态-固定模式-正常状态转移到如果它已经收到足够的奖励-选择基于贪婪策略的行动，消除行动选择的随机性，提高效率。首先，我们提出了一个算法与惩罚相结合，避免合理的决策和在线利润分享固定的模式状态。然后，我们讨论了目标系统和学习控制器的设计。在仿真中，学习任务包括通过使用学习控制器来修改机器人的腰部轨迹来稳定步行。然后，我们讨论了模拟结果和我们的建议的有效性。
In reinforcement learning of long-term tasks, learning efficiency may deteriorate when an agent’s probabilistic actions cause too many mistakes before task learning reaches its goal. The new type of state we propose –fixed mode– to which a normal state shifts if it has already received sufficient reward – chooses an action based on a greedy strategy, eliminating randomness of action selection and increasing efficiency. We start by proposing the combining of an algorithm with penalty avoiding rational policy making and online profit sharing with fixed mode states. We then discuss the target system and learning-controller design. In simulation, the learning task involves stabilizing of biped walking by using the learning controller to modify a robot’s waist trajectory. We then discuss simulation results and the effectiveness of our proposal.