Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
CQ, CS (Joint) |
2024-05-16 14:55 |
Aichi |
(Primary: On-site, Secondary: Online) |
Force Adjustment Control in Cooperative Work between Remote Robot Systems with Force Feedback
-- Application of Reinforcement Learning -- Hitoshi Ohnishi (OUJ), Hiroya Kato, Yutaka Ishibashi (Nagoya Institute of Technology), Pingguo Huang (Gifu Shotoku Gakuen Univ.) |
(To be available after the conference date) [more] |
|
NS |
2024-05-09 14:15 |
Mie |
Sinfonia Technology Hibiki Hall Ise (Primary: On-site, Secondary: Online) |
[Encouragement Talk]
Improvement on the Dueling Networks Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design Tianchen Zhou (Sophia Univ.), Yutaka Yakuwa (NEC), Natsuki Okamura, Hiroyuki Hochigai (Sophia Univ.), Takayuki Kuroda (NEC), Ikuko E. Yairi (Sophia Univ.) |
(To be available after the conference date) [more] |
|
CAS, CS |
2024-03-14 16:20 |
Okinawa |
|
An Approximate Solution Using K-Shortest Path and Reinforcement Learning for a Load Balancing Problem in Communication Networks Himeno Takahashi, Norihiko Shinomiya (Soka Univ.) CAS2023-123 CS2023-116 |
In recent years, the amount of data traffic in information and communication networks has been increasing and the risk o... [more] |
CAS2023-123 CS2023-116 pp.70-73 |
KBSE |
2024-03-14 15:40 |
Okinawa |
Okinawa Prefectual General Welfare Center (Primary: On-site, Secondary: Online) |
An approach for improving perceived safety in autonomous driving using personalized shielding Ryotaro Abe, Jialong Li, Jinyu Cai (Waseda Univ.), Shinichi Honiden (NII), Kenji Tei (Tokyo Tech) KBSE2023-76 |
This research introduces an innovative Reinforcement Learning (RL) approach tailored for autonomous driving systems, ter... [more] |
KBSE2023-76 pp.67-72 |
SIS |
2024-03-15 12:20 |
Kanagawa |
Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
Extension of decision transformer model for controlling future reward and motion control Taisei Inada, Shigeru Kubota (Yamagata Univ.) SIS2023-58 |
In the field of control, reinforcement learning is sometimes required to dynamically adjust the speed of automatic drivi... [more] |
SIS2023-58 pp.73-76 |
RCS, SR, SRW (Joint) |
2024-03-15 16:15 |
Tokyo |
The University of Tokyo (Hongo Campus), and online (Primary: On-site, Secondary: Online) |
Study on Small Cell ON/OFF Control Using Different Frequency Cell Information Takaharu Kobayashi, Takashi Dateki (Fujitsu) RCS2023-292 |
In this paper, we propose small cell ON/OFF control without using UE position information and information on the proximi... [more] |
RCS2023-292 pp.176-181 |
PRMU, IBISML, IPSJ-CVIM |
2024-03-03 17:00 |
Hiroshima |
Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Multi-agent reinforcement learning based control method for large-scale crowd movement on Mojiko Fireworks Festival dataset Kazuya Miyazaki, Masato Kiyama, Motoki Amagasaki, Toshiaki Okamoto (Kumamoto Univ.) IBISML2023-45 |
The importance of human flow guidance is increasing in response to accidents at events. In recent years, some research h... [more] |
IBISML2023-45 pp.36-43 |
AI |
2024-03-01 13:40 |
Aichi |
Room0221, Bldg.2-C, Nagoya Institute of Technology |
Applying Graph Neural Networks and Reinforcement Learning to the Multiple Depot-Multiple Traveling Salesman Problem Dongyeop Kim, Toshihiro Matsui (NITech) AI2023-39 |
In this study, we introduce a method combining Graph Neural Networks (GNN) and reinforcement learning for the Multiple D... [more] |
AI2023-39 pp.13-18 |
AI |
2024-03-01 15:00 |
Aichi |
Room0221, Bldg.2-C, Nagoya Institute of Technology |
Performance Improvement for Mobile Edge Computing with Multi-Agent Deep Reinforcement Learning Kohei Suzuki, Toshiharu Sugawara (Waseda Univ.) AI2023-42 |
In this paper, we propose a method for mobile edge computing using unmanned aerial vehicles (UAVs) to improve both the n... [more] |
AI2023-42 pp.31-36 |
NS, IN (Joint) |
2024-03-01 11:35 |
Okinawa |
Okinawa Convention Center |
Application of a Deep Reinforcement Learning Algorithm to Virtual Machine Migration Control in Multi-Stage Information Processing Systems Yuki Kojitani (Okayama Univ.), Kazutoshi Nakane (Nagoya Univ.), Yuya Tarutani (Okayama Univ.), Celimuge Wu (UEC), Yusheng Ji (NII), Tokumi Yokohira (Okayama Univ.), Tutomu Murase (Nagoya Univ.), Yukinobu Fukushima (Okayama Univ.) IN2023-87 |
This paper tackles a virtual machine (VM) migration control problem to maximize the progress (accuracy) of information p... [more] |
IN2023-87 pp.130-135 |
SR |
2024-01-25 13:10 |
Nagano |
Nagano-ken JA building (Primary: On-site, Secondary: Online) |
[Short Paper]
Performance Evaluations on Deep Reinfocement Leanring based Analog Beamforming in Dynamic Senarios Daisuke Sasaki, Xiaoyan Wang, Zhou Hang (Ibaraki Univ.), Umehira Masahiro (Nanzan Univ.) SR2023-72 |
As the development of small cell architecture in B5G networks, on one hand, the frequency utilization efficiency could b... [more] |
SR2023-72 pp.22-24 |
SR |
2024-01-25 13:25 |
Nagano |
Nagano-ken JA building (Primary: On-site, Secondary: Online) |
[Short Paper]
A Performance Evaluation on Deep Reinforcement Learning based Transmit Power Control for Uplink NOMA Kaito Sawada, Xiaoyan Wang, Zhou Hang (Ibaraki Univ.), Masahiro Umehira (Nanzan Univ.) SR2023-73 |
Non-Orthogonal Multiple Access (NOMA) technology has attracted much attention in order to improve frequency utilization ... [more] |
SR2023-73 pp.25-27 |
PRMU, MVE, VRSJ-SIG-MR, IPSJ-CVIM |
2024-01-25 14:40 |
Kanagawa |
Keio Univ. (Hiyoshi Campus) |
Efficient exploration with intrinsic motivation considering state transitions in deep reinforcement learning Kaito Ohshika, Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2023-42 |
In deep reinforcement learning, learning data is collected through the interaction between the agent and the environment... [more] |
PRMU2023-42 pp.14-19 |
IA |
2024-01-25 16:40 |
Tokyo |
Kwansei Gakuin Univiversity, Marunouchi Campus (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Study on Routing Schemes Using Reinforcement Learning with Cooperation for IoT Applications Takahashi Shotaro, Inoue Shota, Ohsaki Hiroyuki (Kwansei Univ) IA2023-71 |
Low-power and Lossy Networks (LLN) have attracted much attention as wireless networks for Internet of Things (IoT) appli... [more] |
IA2023-71 pp.59-64 |
SS, MSS |
2024-01-17 14:30 |
Ishikawa |
(Primary: On-site, Secondary: Online) |
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator Ryoma Furuyama, Daiki Kuyoshi, Yamane Satoshi (Kanazawa Univ.) MSS2023-55 SS2023-34 |
Imitation learning is often used in addition to reinforcement learning in environments where reward design is difficult ... [more] |
MSS2023-55 SS2023-34 pp.19-24 |
AP, WPT (Joint) |
2024-01-18 14:00 |
Niigata |
Tokimate, Niigata University (Primary: On-site, Secondary: Online) |
[Tutorial Lecture]
Reinforcement learning and its computer simulation Hitoshi Kono (Tokyo Denki Univ.) AP2023-170 |
Reinforcement learning is a learning algorithm in which an agent selects actions through trial and error and explores fo... [more] |
AP2023-170 pp.58-61 |
SS, MSS |
2024-01-18 11:30 |
Ishikawa |
(Primary: On-site, Secondary: Online) |
Deep Reinforcement Learning Using LMM's Studying Papers and Intrinsic Rewards Sota Nagano, Satoshi Yamane (Kanazawa Univ.) MSS2023-64 SS2023-43 |
Research combining deep reinforcement learning with a large language model (LLM) produced high scores even for open-worl... [more] |
MSS2023-64 SS2023-43 pp.70-75 |
DE, IPSJ-DBS |
2023-12-26 14:20 |
Tokyo |
Institute of Industrial Science, The University of Tokyo |
A study on selective reuse of local policies in transfer learning agents Hiroya Hamada, Fumiaki Saitoh (CIT) DE2023-29 |
In recent years, reinforcement learning has gained attention for its application in acquiring AI behaviors. One challeng... [more] |
DE2023-29 pp.7-11 |
NS, RCS (Joint) |
2023-12-14 16:50 |
Fukuoka |
Kyushu Institute of Technology Tobata campus, and Online (Primary: On-site, Secondary: Online) |
Dueling Networks Architecture in the Deep Reinforcement Learning for the Automated ICT System Design Tianchen Zhou (Sophia Univ.), Yutaka Yakuwa (NEC), Natsuki Okamura, Hiroyuki Hochigai (Sophia Univ.), Takayuki Kuroda (NEC), Ikuko E. Yairi (Sophia Univ.) NS2023-135 |
This paper presents a reinforcement learning based approach for the design of ICT systems. The automated ICT system desi... [more] |
NS2023-135 pp.54-59 |
NS, RCS (Joint) |
2023-12-15 11:45 |
Fukuoka |
Kyushu Institute of Technology Tobata campus, and Online (Primary: On-site, Secondary: Online) |
Deep Reinforcement Learning Based Computing Resource Allocation in Fog Radio Access Networks Tong Zhaowei (Kyushu Univ.), Ahmad Gendia (Al-Azhar Univ.), Osamu Muta (Kyushu Univ.) RCS2023-198 |
The integration of artificial intelligence (AI) with fog radio access networks (F-RANs) has garnered significant interes... [more] |
RCS2023-198 pp.112-117 |