Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes
| Autor: | Hyun-Soo Lee, Jinbae Kim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
reinforcement learning Computer science Process (engineering) Learning object Topology (electrical circuits) competitive network agent 02 engineering and technology Network topology Topology lcsh:Technology lcsh:Chemistry 020901 industrial engineering & automation dynamically changes networks adaptive algorithm 0202 electrical engineering electronic engineering information engineering Reinforcement learning General Materials Science lcsh:QH301-705.5 Instrumentation Fluid Flow and Transfer Processes Adaptive algorithm lcsh:T Process Chemistry and Technology Learning environment General Engineering Complex network lcsh:QC1-999 Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 human–machine–agent interaction 020201 artificial intelligence & image processing lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences Volume 10 Issue 17 Applied Sciences, Vol 10, Iss 5828, p 5828 (2020) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10175828 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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