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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 26  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
CCS, IN
(Joint)
2023-08-03
11:09
Hokkaido Banya-no-yu Reinforcement learning-based control of CWmin and Carrier Sense Threshold for IEEE 802.11 WLAN.
Yuto Higashiyama, Kosuke Sanada, Hiroyuki Hatano, Kazuo Mori (Mie Univ.) CCS2023-20
Distribute Coordination Function (DCF) is a basic channel access protocol in IEEE 802.11 Wireless Local Area Networks (W... [more] CCS2023-20
pp.19-24
SAT, RCS
(Joint)
2022-08-26
09:50
Hokkaido
(Primary: On-site, Secondary: Online)
Joint UAV Positioning and User Pairing via Reinforcement Learning Based Selection in NOMA Systems
Ahmad Gendia, Osamu Muta (Kyushu Univ.), Sherief Hashima (RIKEN-AIP), Kohei Hatano (Kyushu Univ.) RCS2022-114
This paper proposes two reinforcement learning (RL)-based algorithms for millimeter wave (mmWave)-enabled unmanned aeria... [more] RCS2022-114
pp.96-101
NS 2022-04-15
10:45
Tokyo kikai shinkou kaikan + online
(Primary: On-site, Secondary: Online)
[Encouragement Talk] An Actor-Critic based Reinforcement Learning Algorithm for Combinatorial Optimization and Mobile Power Trucks Routing Problem
Zhao Wang (NTT), Yuhei Senuma (Waseda Univ.), Yuusuke Nakano (NTT), Jun Ohya (Waseda Univ.), Ken Nishimatsu (NTT) NS2022-1
In this paper, we propose an Actor-Critic based reinforcement learning (RL) algorithm for solving the traditional combin... [more] NS2022-1
pp.1-6
MBE, NC
(Joint)
2021-10-28
16:20
Online Online Study on rounding error and Learning performance of reinforcement learning model for FPGA implementation
Daisuke Oguchi, Satoshi Moriya, Hideaki Yamamoto, Shigeo Sato (Tohoku Univ) NC2021-24
In recent years, the hardware implementation of reinforcement learning (RL) has attracted attention due to its wide rang... [more] NC2021-24
pp.34-39
RCS, SAT
(Joint)
2019-08-23
10:20
Aichi Nagoya University Blockage Detection and User Association in mmWave Networks
Yuva Kumar S., Tomoaki Ohtsuki (Keio Univ.) RCS2019-163
The large spectral bandwidth at millimeter-wave (mmWave) frequencies provides a mean to achieve very high data rates in ... [more] RCS2019-163
pp.91-96
RCS 2019-06-19
14:55
Okinawa Miyakojima Hirara Port Terminal Building Policy Gradient Reinforcement Learning for Reducing Transmission Delay in EDCA
Masao Shinzaki, Yusuke Koda, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.) RCS2019-52
This paper proposes a packet mapping algorithm among Access Categories (ACs) in Enhanced Distributed Channel Access (EDC... [more] RCS2019-52
pp.91-96
CCS 2018-11-23
10:50
Hyogo Kobe Univ. A Human-Like Agent Based on a Hybrid of Reinforcement and Imitation Learning
Xinyu Lian, Rousslan Fernand Julien Dossa (Kobe Univ.), Hirokazu Nomoto (EQUOS RESEARCH), Takashi Matsubara, Kuniaki Uehara (Kobe Univ.) CCS2018-41
Reinforcement learning (RL) makes it possible to build an efficient agent that handles tasks in complex and uncertain en... [more] CCS2018-41
pp.45-50
SAT, RCS
(Joint)
2018-08-10
15:45
Iwate Iwate University Blockage Aware Beam Allocation in mmWave Using Reinforcement Learning
Yuva Kumar S., Fereidoun H. Panahi, Tomoaki Ohtsuki (Keio Univ.) RCS2018-149
With the advent of the fifth generation (5G) systems, there is an increasing demand for high data rate transmission and ... [more] RCS2018-149
pp.99-104
MoNA, ASN, IPSJ-MBL, IPSJ-UBI [detail] 2018-02-27
14:15
Tokyo Sophia University Coverage Expansion in mmWave V2I Communications by Deep Reinforcement Learning Based Vehicle Re-deployment
Akihito Taya, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.) MoNA2017-69
The small coverage of road side units (RSUs) is one of the challenges in millimeter wave (mmWave) communications for aut... [more] MoNA2017-69
pp.317-322
MoNA 2018-01-19
11:15
Kyoto Campus Plaza Kyoto Decentralized WLAN Access Point Selection through Reinforcement Learning
Takuya Nakamura, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.), Toshihisa Nabetani (TOSHIBA) MoNA2017-51
Many operators provide public wireless LAN services in public places such as stations or cafes. In many cases, a station... [more] MoNA2017-51
pp.57-62
MBE, NC
(Joint)
2017-03-13
10:25
Tokyo Kikai-Shinko-Kaikan Bldg. Estimation of the change of agent's behavior strategy using state-action history
Shihori Uchida, Shigeyuki Oba, Shin Ishii (Kyoto Univ.) NC2016-65
Reinforcement learning (RL) is a model of learning process of animals and intelligent agents to obtain the optimal behav... [more] NC2016-65
pp.7-12
RCS, SR, SRW
(Joint)
2013-03-01
14:00
Tokyo Waseda Univ. A Reinforcement Learning Based Sensing Policy for Cognitive Radio Systems
Fereidoun H. Panahi, Tomoaki Ohtsuki (Keio Univ.) RCS2012-363
Spectrum sensing is a critical function for enabling dynamic spectrum access (DSA) in wireless networks that utilize cog... [more] RCS2012-363
pp.471-476
IBISML 2012-11-07
15:30
Tokyo Bunkyo School Building, Tokyo Campus, Tsukuba Univ. Stochastic policy gradient method for a stochastic policy using a Gaussian process regression
Yutaka Nakamura, Hiroshi Ishiguro (Osaka Univ.) IBISML2012-52
Reinforcement learning (RL) methods using Gaussian process regression (GP) for approximating the value function have bee... [more] IBISML2012-52
pp.129-133
SR, AN, USN, RCS
(Joint)
2012-10-19
14:55
Fukuoka Fukuoka univ. A Fuzzy Q-Learning Based Sensing Policy for Cognitive Radio Systems
Fereidoun H. Panahi, Tomoaki Ohtsuki (Keio Univ.) RCS2012-155
In a cognitive radio (CR) network, the channel sensing scheme to detect the appearance of a primary user (PU) directly a... [more] RCS2012-155
pp.173-178
RCS 2012-06-21
13:50
Hokkaido Hakodate City Central Library Cell Range Expansion Using Distributed Q-Learning in Heterogeneous Networks
Toshihito Kudo, Tomoaki Ohtsuki (Keio Univ.) RCS2012-51
Heterogeneous networks (HetNets) that put pico base stations (PBSs) in the macro cells are necessary to improve the netw... [more] RCS2012-51
pp.49-54
RCS, SIP 2012-01-27
14:20
Fukuoka Fukuoka Univ. Learning-based Cell Selection for Open-access Femtocell Networks
Chaima Dhahri, Tomoaki Ohtsuki (Keio Univ.) SIP2011-121 RCS2011-310
In an open-access femtocell networks, nearby cellular users (Macro User: MU) may join, through a handover procedure, one... [more] SIP2011-121 RCS2011-310
pp.245-250
NC 2012-01-27
13:30
Hokkaido Future University Hakodate Analysis of Time Series Data Accompanied with Rewards and Actions using Reinforcement Learning
Hideki Asoh, Masanori Shiro, Toshihiro Kamishima, Shotaro Akaho (AIST), Takahide Kohro (Univ. Tokyo) NC2011-115
Although the main applications of reinforcement learning (RL) is online
learning of intelligent agents working in envir... [more]
NC2011-115
pp.107-112
MSS 2011-01-20
13:20
Yamaguchi Kaikyo-Messe-Shimonoseki Reinforcement Learning with Conditioned Rule Updating to Prevent Conflicts during the Allocation of Tasks
Alex Valdivielso, Toshiyuki Miyamoto (Osaka Univ.) CST2010-65
In many applications, effective task-allocation and task-completion strategies are crucial to achieve an optimal perform... [more] CST2010-65
pp.33-38
IBISML 2010-06-15
15:25
Tokyo Takeda Hall, Univ. Tokyo New Feature Selection Method for Reinforcement Learning -- Conditional Mutual Information Reveals Implicit State-Reward Dependency --
Hirotaka Hachiya, Masashi Sugiyama (Tokyo Insst. of Tech.) IBISML2010-21
Model-free reinforcement learning (RL) is a machine learning approach to decision making in unknown environment. However... [more] IBISML2010-21
pp.139-146
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