| Presentation | 2018-11-23 A Human-Like Agent Based on a Hybrid of Reinforcement and Imitation Learning Xinyu Lian, Rousslan Fernand Julien Dossa, Hirokazu Nomoto, Takashi Matsubara, Kuniaki Uehara, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Reinforcement learning (RL) makes it possible to build an efficient agent that handles tasks in complex and uncertain environments by maximizing future reward. However, for applications in some areas like game AI and autonomous driving, efficiency only cannot satisfy the practical use, and a human-like agent is preferable. On the other hand, in imitation learning (IL) tasks, which trains the agent to mimic actions of expert behavior provided as training data and thereby learns relatively complex tasks while achieving human-like behavior. Unfortunately, the performance of such an agent is generally limited by the expert behavior. Thus, with the aim of training an agent which achieves high performance while retaining a human-like behavior, we propose a method for mixing RL and IL, applicable to both discrete and continuous problems. We used state-of-the-art RL and IL algorithms and trained their respective models independently, before mixing them into the proposed hybrid model. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Human-LikeHybrid ModelReinforcement LearningImitation LearningGame AIAutonomous Driving |
| Paper # | CCS2018-41 |
| Date of Issue | 2018-11-15 (CCS) |
| Conference Information | |
| Committee | CCS |
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| Conference Date | 2018/11/22(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kobe Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Interaction and Communication, etc. |
| Chair | Mikio Hasegawa(Tokyo Univ. of Science) |
| Vice Chair | Makoto Naruse(NICT) / Shigeki Shiokawa(Kanagawa Inst. of Tech.) |
| Secretary | Makoto Naruse(Tokyo City Univ.) / Shigeki Shiokawa(Hiroshima City Univ.) |
| Assistant | Yusuke Kawakita(Kanagawa Inst. of Tech.) / Hiroyasu Ando(Tsukuba Univ.) / Takashi Matsubara(Kobe Univ.) / Ryo Takahashi(AUT) |
| Paper Information | |
| Registration To | Technical Committee on Complex Communication Sciences |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Human-Like Agent Based on a Hybrid of Reinforcement and Imitation Learning |
| Sub Title (in English) | |
| Keyword(1) | Human-LikeHybrid ModelReinforcement LearningImitation LearningGame AIAutonomous Driving |
| 1st Author's Name | Xinyu Lian |
| 1st Author's Affiliation | Kobe University(Kobe Univ.) |
| 2nd Author's Name | Rousslan Fernand Julien Dossa |
| 2nd Author's Affiliation | Kobe University(Kobe Univ.) |
| 3rd Author's Name | Hirokazu Nomoto |
| 3rd Author's Affiliation | EQUOS RESEARCH Co., Ltd.(*) |
| 4th Author's Name | Takashi Matsubara |
| 4th Author's Affiliation | Kobe University(Kobe Univ.) |
| 5th Author's Name | Kuniaki Uehara |
| 5th Author's Affiliation | Kobe University(Kobe Univ.) |
| Date | 2018-11-23 |
| Paper # | CCS2018-41 |
| Volume (vol) | vol.118 |
| Number (no) | CCS-316 |
| Page | pp.pp.45-50(CCS), |
| #Pages | 6 |
| Date of Issue | 2018-11-15 (CCS) |