Exploration Method for Reducing Uncertainty using Q-entropy in Deep Reinforcement Learning
- Issue Date
- ICGHIT 프러시딩, v.1, no.1, pp.269 - 271
- Journal Title
- ICGHIT 프러시딩
- Start Page
- End Page
- In this paper, we propose a novel exploration method for a Q-learning based deep reinforcement learning. The agent decides whether to explore or exploit according to the uncertainty on current state. To measure the amount of uncertainty, we utilize entropy value of action-values at each state. That is, the agent explore with random actions when the entropy of action-values is high or does greedy action when the entropy is low. Also we adopt the state visit-counter to handle the ambiguous states which means that several optimal actions exist.
- Files in This Item
- There are no files associated with this item.
- Appears in
- College of Science and Technology > Department of Computer and Information Communications Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.