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 |