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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 346  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
IA, SITE, IPSJ-IOT [detail] 2024-03-13
15:30
Okinawa Miyakojima City Future Creation Center
(Primary: On-site, Secondary: Online)
Access control through topic analysis considering with intents
Yuta Kariya, Tetsuya Morizumi, Kaito Hosono, Hirotsugu Kinoshita (Kanagawa Univ.) SITE2023-107 IA2023-113
(To be available after the conference date) [more] SITE2023-107 IA2023-113
pp.247-254
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 in Mobile Edge Computing Using 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
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
15:15
Fukuoka Kyushu Institute of Technology Tobata campus, and Online
(Primary: On-site, Secondary: Online)
Investigation on Shortening the Time to Fix Transmission Timing in Wireless Sensor Networks Using Reinforcement Learning
Kureha Ikeda, Yasushi Fuwa, David Asano (Shinshu Univ.) NS2023-149
This study proposes a novel approach to optimizing transmission timing in Wireless Sensor Networks (WSNs) by applying re... [more] NS2023-149
pp.132-137
HCGSYMPO
(2nd)
2023-12-11
- 2023-12-13
Fukuoka Asia pacific Import Mart (Kitakyushu)
(Primary: On-site, Secondary: Online)
Transition and analysis by mutual learning within a group in incomplete information game in " Hol's der Geier"
Shintaro Abe, Kazuki Takahashi, Takashi Takekawa (Kogakuin Univ)
In perfect information games, AI learned through self-play and achieved high performance. In incomplete information game... [more]
NC, MBE
(Joint)
2023-11-27
10:30
Osaka Kindai Univ.
(Primary: On-site, Secondary: Online)
Improving the reproduction of animal intelligence using reinforcement learning with World Model
Takumi Fukaya, Hirokazu Tanaka (Tokyo City Univ.) NC2023-34
One way to evaluate artificial intelligence models that reproduce animal intelligence is to have reinforcement learning ... [more] NC2023-34
pp.6-9
 Results 1 - 20 of 346  /  [Next]  
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