We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Learning Agents in Robot Navigation: Trends and Next Challenges.
- Authors
Uwano, Fumito
- Abstract
Multiagent reinforcement learning performs well in multiple situations such as social simulation and data mining. It particularly stands out in robot control. In this approach, artificial agents behave in a system and learn their policies for their own satisfaction and that of others. Robots encode policies to simulate the performance. Therefore, learning should maintain and improve system performance. Previous studies have attempted various approaches to outperform control robots. This paper provides an overview of multiagent reinforcement learning work, primarily on navigation. Specifically, we discuss current achievements and limitations, followed by future challenges.
- Subjects
REINFORCEMENT learning; ROBOT control systems; ROBOTS; DATA mining; NAVIGATION; SATISFACTION
- Publication
Journal of Robotics & Mechatronics, 2024, Vol 36, Issue 3, p508
- ISSN
0915-3942
- Publication type
Article
- DOI
10.20965/jrm.2024.p0508