| Presentation | 2021-11-19 Toward Human Cognition-inspired High-Level Decision Making For Hierarchical Reinforcement Learning Agents Rousslan Fernand Julien Dossa, Takashi Matsubara, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Hierarchical reinforcement learning (HRL) methods aim to leverage the concept of temporal abstraction to efficiently solve long-horizon, sequential decision-making problems with sparse and delayed rewards. However, the decision-making process of the agent in most HRL methods is often based directly on low-level observations, while also using fixed temporal abstraction. We propose the hierarchical world model (HWM), which can capture more flexible high-level, temporally abstract dynamics, as well as low-level dynamics of the system. We posit such model is a natural extension to the HRL framework toward a decision-making process closer to that of humans. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Reinforcement learning / Hierarchical reinforcement learning / World models / Temporal abstraction / Hierarchically organized behavior |
| Paper # | CCS2021-28 |
| Date of Issue | 2021-11-11 (CCS) |
| Conference Information | |
| Committee | CCS |
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| Conference Date | 2021/11/18(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Osaka Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Tetsuya Asai(Hokkaido Univ.) |
| Vice Chair | Megumi Akai(Hokkaido Univ.) / Masaki Aida(TMU) |
| Secretary | Megumi Akai(TDK) / Masaki Aida(Mie Univ.) |
| Assistant | Hidehiro Nakano(Tokyo City Univ.) / Hiroyasu Ando(Tsukuba Univ.) / Takashi Matsubara(Osaka Univ.) / Sumiko Miyata(Shibaura Inst. of Tech.) |
| 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) | Toward Human Cognition-inspired High-Level Decision Making For Hierarchical Reinforcement Learning Agents |
| Sub Title (in English) | |
| Keyword(1) | Reinforcement learning |
| Keyword(2) | Hierarchical reinforcement learning |
| Keyword(3) | World models |
| Keyword(4) | Temporal abstraction |
| Keyword(5) | Hierarchically organized behavior |
| 1st Author's Name | Rousslan Fernand Julien Dossa |
| 1st Author's Affiliation | Kobe University(Kobe Univ.) |
| 2nd Author's Name | Takashi Matsubara |
| 2nd Author's Affiliation | Osaka University(Osaka Univ.) |
| Date | 2021-11-19 |
| Paper # | CCS2021-28 |
| Volume (vol) | vol.121 |
| Number (no) | CCS-253 |
| Page | pp.pp.61-66(CCS), |
| #Pages | 6 |
| Date of Issue | 2021-11-11 (CCS) |