강화학습기법의 동적일정계획에의 적용가능성에 대한 소고
Feasibility Study on the Reinforcement Learning for a Dynamic Scheduling Problem
2
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
The ability to dynamically reschedule jobs is core of flexible manufacturing system. Reinforcement learning, a machine learning approach undergoing development in various control systems, offers advantages in dynamic environments. This paper presents a feasibility study on the reinforcement learning for a dynamic scheduling problem.
Abstract
The ability to dynamically reschedule jobs is core of flexible manufacturing system. Reinforcement learning, a machine learning approach undergoing development in various control systems, offers advantages in dynamic environments. This paper presents a feasibility study on the reinforcement learning for a dynamic scheduling problem.
참고문헌 (5)
- Bertsekas, D. P., "Neuro-dynamic programming" (1996), Athena Scientific
- Kaelbling, L. P., "Reinforcement learning: A survey", J. Artif. Intell. Res., 4, pp.237~285 (1996)
- Pinedo, M., "Scheduling: Theory, Algorithms, and Systems" (1995), Prentice Hall
- Sutton, R. S., "Reinforcement learning-An introduction" (1998), MIT Press
- Tsitsiklis, J., "Asynchronous stochasitc approximation and Q-learning", Machine Learning, 16(3), pp.185~202 (1994)