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. 2022 Dec 22;23(1):98.
doi: 10.3390/s23010098.

Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents

Minwoo Kim  1   2 , Jinhee Bae  3 , Bohyun Wang  1 , Hansol Ko  1 , Joon S Lim  1
Affiliations

Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents

Minwoo Kim et al. Sensors (Basel). .

Abstract

In this study, we propose a method to automatically find features from a dataset that are effective for classification or prediction, using a new method called multi-agent reinforcement learning and a guide agent. Each feature of the dataset has one of the main and guide agents, and these agents decide whether to select a feature. Main agents select the optimal features, and guide agents present the criteria for judging the main agents' actions. After obtaining the main and guide rewards for the features selected by the agents, the main agent that behaves differently from the guide agent updates their Q-values by calculating the learning reward delivered to the main agents. The behavior comparison helps the main agent decide whether its own behavior is correct, without using other algorithms. After performing this process for each episode, the features are finally selected. The feature selection method proposed in this study uses multiple agents, reducing the number of actions each agent can perform and finding optimal features effectively and quickly. Finally, comparative experimental results on multiple datasets show that the proposed method can select effective features for classification and increase classification accuracy.

Keywords: feature selection; guide agents; main agents; multi-agent; reinforcement learning (RL); rewards.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Illustration of Multi-Agent Reinforcement Learning Feature Selection Method with Guide Agents (MARFS-GA).
Figure 2
Figure 2
Framework Overview.
Figure 3
Figure 3
Initializing Main Agents and Guide Agents.
Figure 4
Figure 4
Training Strategy.
Figure 5
Figure 5
Number of selected features per episode, for each dataset.
Figure 6
Figure 6
Accuracy of selected features per episode, for each dataset.
Figure 6
Figure 6
Accuracy of selected features per episode, for each dataset.
See this image and copyright information in PMC

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