Effective action selection under multi task by ignoring tasks and limiting tasks
Takuya Masaki, Kentarou Kurashige
- Year
- 2016
- Citations
- 3
Abstract
In recent years, research has been conducted to treat multitask. In this field, task priorities change optimal solution and varies according to the situation. There is the problem that when priority is low, It can't do action selection well. We solve these problem by restrict tasks. We made a proposal that two types of restriction. Thereby, we assumed to can select action according to the situation. We carried out an experiment that set three tasks to a robot applied proposal technique. From results of the experiment, we showed the proposed technique improved learning performance and confirmed the usefulness of proposal technique.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991