A comparative study of policies in Q-learning for foraging tasks
Q-learning is a machine learning technique that learns what to do and how to map states to actions to maximize rewards. Q-learning has been applied to various tasks such as foraging, soccer and prey-pursuing robots. In this paper, a simple foraging task has been considered to study the influences of...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | |
| Published: |
2017
|
| Online Access: | http://dspace.uniten.edu.my/jspui/handle/123456789/6522 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Q-learning is a machine learning technique that learns what to do and how to map states to actions to maximize rewards. Q-learning has been applied to various tasks such as foraging, soccer and prey-pursuing robots. In this paper, a simple foraging task has been considered to study the influences of the policies reported in the open literatures. A mobile robot is used to search and retrieve pucks back to a home location. The goal of this study is to identify an efficient policy for q-learning which maximizes the number of pucks collected and minimizes the number of collisions in the environment. Policies namely greedy, epsilon-greedy, Boltzmann distribution and random search are used to study their performances in the foraging task and the results are presented. ©2009 IEEE. |
|---|