COMMON-SENSICAL INCENTIVE REWARD IN DEEP ACTOR-CRITIC REINFORCEMENT LEARNING FOR MOBILE ROBOT NAVIGATION
Recently, various Deep Actor -Critic Reinforcement Learning (DAC-RL) algorithms have been widely utilized for training mobile robots in acquiring navigational policies. However, they usually need a preventively long learning time to achieve good policies. This research proposes a two -stage training...
| Published in: | INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
ICIC INT
2024
|
| Subjects: | |
| Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001204092500009 |