Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes
In recent years, the problem of reinforcement learning has become increasingly complex, and the computational demands with respect to such processes have increased. Accordingly, various methods for effective learning have been proposed. With the help of humans, the learning object can learn more acc...
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MDPI AG
2020-08-01
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Online Access: | https://www.mdpi.com/2076-3417/10/17/5828 |
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author | Jinbae Kim Hyunsoo Lee |
author_facet | Jinbae Kim Hyunsoo Lee |
author_sort | Jinbae Kim |
collection | DOAJ |
description | In recent years, the problem of reinforcement learning has become increasingly complex, and the computational demands with respect to such processes have increased. Accordingly, various methods for effective learning have been proposed. With the help of humans, the learning object can learn more accurately and quickly to maximize the reward. However, the rewards calculated by the system and via human intervention (that make up the learning environment) differ and must be used accordingly. In this paper, we propose a framework for learning the problems of competitive network topologies, wherein the environment dynamically changes agent, by computing the rewards via the system and via human evaluation. The proposed method is adaptively updated with the rewards calculated via human evaluation, making it more stable and reducing the penalty incurred while learning. It also ensures learning accuracy, including rewards generated from complex network topology consisting of multiple agents. The proposed framework contributes to fast training process using multi-agent cooperation. By implementing these methods as software programs, this study performs numerical analysis to demonstrate the effectiveness of the adaptive evaluation framework applied to the competitive network problem depicting the dynamic environmental topology changes proposed herein. As per the numerical experiments, the greater is the human intervention, the better is the learning performance with the proposed framework. |
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issn | 2076-3417 |
language | English |
last_indexed | 2024-03-10T16:57:54Z |
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spelling | doaj.art-467b4cfe3ae34c55a8f6f5fac8f509162023-11-20T11:04:14ZengMDPI AGApplied Sciences2076-34172020-08-011017582810.3390/app10175828Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology ChangesJinbae Kim0Hyunsoo Lee1School of Industrial Engineering, Kumoh National Institute of Technology, Gumi 39177, KoreaSchool of Industrial Engineering, Kumoh National Institute of Technology, Gumi 39177, KoreaIn recent years, the problem of reinforcement learning has become increasingly complex, and the computational demands with respect to such processes have increased. Accordingly, various methods for effective learning have been proposed. With the help of humans, the learning object can learn more accurately and quickly to maximize the reward. However, the rewards calculated by the system and via human intervention (that make up the learning environment) differ and must be used accordingly. In this paper, we propose a framework for learning the problems of competitive network topologies, wherein the environment dynamically changes agent, by computing the rewards via the system and via human evaluation. The proposed method is adaptively updated with the rewards calculated via human evaluation, making it more stable and reducing the penalty incurred while learning. It also ensures learning accuracy, including rewards generated from complex network topology consisting of multiple agents. The proposed framework contributes to fast training process using multi-agent cooperation. By implementing these methods as software programs, this study performs numerical analysis to demonstrate the effectiveness of the adaptive evaluation framework applied to the competitive network problem depicting the dynamic environmental topology changes proposed herein. As per the numerical experiments, the greater is the human intervention, the better is the learning performance with the proposed framework.https://www.mdpi.com/2076-3417/10/17/5828adaptive algorithmcompetitive network agentdynamically changes networkshuman–machine–agent interactionreinforcement learning |
spellingShingle | Jinbae Kim Hyunsoo Lee Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes Applied Sciences adaptive algorithm competitive network agent dynamically changes networks human–machine–agent interaction reinforcement learning |
title | Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes |
title_full | Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes |
title_fullStr | Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes |
title_full_unstemmed | Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes |
title_short | Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes |
title_sort | cooperative multi agent interaction and evaluation framework considering competitive networks with dynamic topology changes |
topic | adaptive algorithm competitive network agent dynamically changes networks human–machine–agent interaction reinforcement learning |
url | https://www.mdpi.com/2076-3417/10/17/5828 |
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