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Sensors
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Advanced Machine Learning for Massive Sensing Data
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Advanced Machine Learning for Massive Sensing Data

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 1515

Special Issue Editors

Dr. Yunsick Sung

E-Mail Website
Guest Editor
Division of AI Software Convergence, Dongguk University-Seoul, Seoul 04620, Republic of Korea
Interests: demonstration-based learning; deep learning; autonomous things; drones; sensor simulations; motion recognition or estimation
Special Issues, Collections and Topics in MDPI journals
Dr. Sang-Geol Lee

E-Mail Website
Guest Editor
College of Software Convergence, Dongseo University, Busan 47011, Republic of Korea
Interests: deep learning; neural network; computer vision; image processing

Special Issue Information

Dear Colleagues,

Various unmanned and industry systems employ a massive number of sensing data, such as unmanned transportation/vehicle systems, manufacturing automation, energy management/applications and so on. Given the advancements in machine learning algorithm performance, it is now possible to analyze and utilize a large number of sensing data for diverse applications, though the limitations of learning time, cost, etc., must be considered. Deep reinforcement learning algorithms have been particularly utilized in industrial applications to control processes, prevent hazards, improve safety and save energy. However, advanced machine learning must be improved to address the limitations of its application of a massive number of sensing data.

This Special Issue is focused on all kinds of machine learning algorithms handling a large number of sensing data.

Dr. Yunsick Sung
Dr. Sang-Geol Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • policy gradient
  • Advantage Actor-Critic (A2C)
  • Asynchronous Advantage Actor-Critic (A3C)
  • Proximal Policy Optimization (PPO)
  • Trust Region Policy Optimization (TRPO)
  • Deep Deterministic Policy Gradient (DDPG)
  • TD3
  • Soft Actor-Critic (SAC)
  • Deep Q-Network (DQN)

Published Papers (1 paper)

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Research

17 pages, 3578 KiB  
Article
Transformer Decoder-Based Enhanced Exploration Method to Alleviate Initial Exploration Problems in Reinforcement Learning
by Dohyun Kyoung and Yunsick Sung
Sensors 2023, 23(17), 7411; https://doi.org/10.3390/s23177411 - 25 Aug 2023
Cited by 1 | Viewed by 1043
Abstract
In reinforcement learning, the epsilon (ε)-greedy strategy is commonly employed as an exploration technique This method, however, leads to extensive initial exploration and prolonged learning periods. Existing approaches to mitigate this issue involve constraining the exploration range using expert data or utilizing pretrained [...] Read more.
In reinforcement learning, the epsilon (ε)-greedy strategy is commonly employed as an exploration technique This method, however, leads to extensive initial exploration and prolonged learning periods. Existing approaches to mitigate this issue involve constraining the exploration range using expert data or utilizing pretrained models. Nevertheless, these methods do not effectively reduce the initial exploration range, as the exploration by the agent is limited to states adjacent to those included in the expert data. This paper proposes a method to reduce the initial exploration range in reinforcement learning through a pretrained transformer decoder on expert data. The proposed method involves pretraining a transformer decoder with massive expert data to guide the agent’s actions during the early learning stages. After achieving a certain learning threshold, the actions are determined using the epsilon-greedy strategy. An experiment was conducted in the basketball game FreeStyle1 to compare the proposed method with the traditional Deep Q-Network (DQN) using the epsilon-greedy strategy. The results indicated that the proposed method yielded approximately 2.5 times the average reward and a 26% higher win rate, proving its enhanced performance in reducing exploration range and optimizing learning times. This innovative method presents a significant improvement over traditional exploration techniques in reinforcement learning. Full article
(This article belongs to the Special Issue Advanced Machine Learning for Massive Sensing Data)
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