| Presentation | 2018-07-12 [Tutorial Lecture] Reinforcement Learning: Application and Issues Takaki Makino, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Reinforcement learning is a machine learning framework that learns best sequence of actions through trial-and-error in domains where providing sufficient supervised data is infeasible. Despite its success recently attracts a lot of attentions, it is not easy to apply it to a new problem. In this tutorial, we introduce the basic idea of reinforcement learning and explain types of problems that are suitable to reinforcement learning. In addition, we discuss roadblocks and recent techniques, in particular the part of problem formulation, reward design and deep reinforcement learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | reinforcement learning / machine learning / deep reinforcement learning |
| Paper # | RCC2018-51,NS2018-68,RCS2018-113,SR2018-48,ASN2018-45 |
| Date of Issue | 2018-07-04 (RCC, NS, RCS, SR, ASN) |
| Conference Information | |
| Committee | ASN / NS / RCS / SR / RCC |
|---|---|
| Conference Date | 2018/7/11(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hakodate Arena |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Wireless Distributed Network, Machine Learning and AI for Wireless Communications and Networks, M2M (Machine-to-Machine), D2D (Device-to-Device), IoT（Internet of Things）, etc. |
| Chair | Hiraku Okada(Nagoya Univ.) / Yoshikatsu Okazaki(NTT) / Tomoaki Otsuki(Keio Univ.) / Kenta Umebayashi(Tokyo Univ. of Agric. and Tech.) / Kazunori Hayashi(Osaka City Univ.) |
| Vice Chair | Koji Yamamoto(Kyoto Univ.) / Jin Nakazawa(Keio Univ.) / Kazuya Monden(Hitachi) / Akihiro Nakao(Univ. of Tokyo) / Eisuke Fukuda(Fujitsu Labs.) / Satoshi Suyama(NTT DoCoMo) / Fumiaki Maehara(Waseda Univ.) / Masayuki Ariyoshi(NEC) / Suguru Kameda(Tohoku Univ.) / Shunichi Azuma(Nagoya Univ.) / HUAN-BANG LI(NICT) |
| Secretary | Koji Yamamoto(NICT) / Jin Nakazawa(Sophia Univ.) / Kazuya Monden(Kanagawa Inst. of Tech.) / Akihiro Nakao(NTT) / Eisuke Fukuda(Osaka Pref Univ.) / Satoshi Suyama(Hokkaido Univ.) / Fumiaki Maehara(NTT) / Masayuki Ariyoshi(NICT) / Suguru Kameda(ATR) / Shunichi Azuma(Univ. of Electro-Comm.) / HUAN-BANG LI(Kagawa Univ.) |
| Assistant | Masafumi Hashimoto(Osaka Univ.) / Tomoyuki Ota(Hiroshima City Univ.) / Tatsuya Kikuzuki(Fujitu Lab.) / Ryo Nakano(HITACHI) / Yoshifumi Hotta(Mitsubishi Electric) / Kenichi Kashibuchi(NTT) / Kazushi Muraoka(NTT DOCOMO) / Shinsuke Ibi(Osaka Univ.) / Hiroshi Nishimoto(Mitsubishi Electric) / Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Gia Khanh Tran(Tokyo Inst. of Tech.) / Syusuke Narieda(Mie Univ.) / Koji Ohshima(Kozo Keikaku Engineering) / Mai Ohta(Fukuoka Univ.) / Teppei Oyama(Fujitsu Lab.) / Toshinori Kagawa(NICT) / Masateru Ogura(NAIST) |
| Paper Information | |
| Registration To | Technical Committee on Ambient intelligence and Sensor Networks / Technical Committee on Network Systems / Technical Committee on Radio Communication Systems / Technical Committee on Smart Radio / Technical Committee on Reliable Communication and Control |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Tutorial Lecture] Reinforcement Learning: Application and Issues |
| Sub Title (in English) | |
| Keyword(1) | reinforcement learning |
| Keyword(2) | machine learning |
| Keyword(3) | deep reinforcement learning |
| Keyword(4) | |
| 1st Author's Name | Takaki Makino |
| 1st Author's Affiliation | Google Inc.(Google) |
| Date | 2018-07-12 |
| Paper # | RCC2018-51,NS2018-68,RCS2018-113,SR2018-48,ASN2018-45 |
| Volume (vol) | vol.118 |
| Number (no) | RCC-123,NS-124,RCS-125,SR-126,ASN-127 |
| Page | pp.pp.119-119(RCC), pp.151-151(NS), pp.161-161(RCS), pp.129-129(SR), pp.135-135(ASN), |
| #Pages | 1 |
| Date of Issue | 2018-07-04 (RCC, NS, RCS, SR, ASN) |