Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline

Search Results (1)

Search Parameters:
Keywords = NDQN (Multi-Agent DQN)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 6002 KiB  
Article
Multi-Agent Deep Q Network to Enhance the Reinforcement Learning for Delayed Reward System
by Keecheon Kim
Appl. Sci. 2022, 12(7), 3520; https://doi.org/10.3390/app12073520 - 30 Mar 2022
Cited by 13 | Viewed by 5983
Abstract
This study examines various factors and conditions that are related with the performance of reinforcement learning, and defines a multi-agent DQN system (N-DQN) model to improve them. N-DQN model is implemented in this paper with examples of maze finding and ping-pong as examples [...] Read more.
This study examines various factors and conditions that are related with the performance of reinforcement learning, and defines a multi-agent DQN system (N-DQN) model to improve them. N-DQN model is implemented in this paper with examples of maze finding and ping-pong as examples of delayed reward system, where delayed reward occurs, which makes general DQN learning difficult to apply. The implemented N-DQN shows about 3.5 times higher learning performance compared to the Q-Learning algorithm in the reward-sparse environment in the performance evaluation, and compared to DQN, it shows about 1.1 times faster goal achievement speed. In addition, through the implementation of the prioritized experience replay and the implementation of the reward acquisition section segmentation policy, such a problem as positive-bias of the existing reinforcement learning models seldom or never occurred. However, according to the characteristics of the architecture that uses many numbers of actors in parallel, the need for additional research on light-weighting the system for further performance improvement has raised. This paper describes in detail the structure of the proposed multi-agent N_DQN architecture, the contents of various algorithms used, and the specification for its implementation. Full article
(This article belongs to the Topic Artificial Intelligence in Sensors)
Show Figures

Figure 1

Back to TopTop