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Committee Date Time Place Paper Title / Authors Abstract Paper #
CCS 2021-11-19
Osaka Osaka Univ.
(Primary: On-site, Secondary: Online)
Toward Human Cognition-inspired High-Level Decision Making For Hierarchical Reinforcement Learning Agents
Rousslan Fernand Julien Dossa (Kobe Univ.), Takashi Matsubara (Osaka Univ.) CCS2021-28
Hierarchical reinforcement learning (HRL) methods aim to leverage the concept of temporal abstraction to efficiently sol... [more] CCS2021-28
NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] 2019-06-18
Okinawa Okinawa Institute of Science and Technology Hybrid Reinforcement and Imitation Learning for Human-Like Agents
Rousslan Fernand Julien Dossa, Xinyu Lian (Kobe Uni), Hirokazu Nomoto (EQUOS RESEARCH), Takashi Matsubara, Kuniaki Uehara (Kobe Uni) NC2019-16 IBISML2019-14
Reinforcement learning methods achieve performance superior to humans in a wide range of complex tasks and uncertain env... [more] NC2019-16 IBISML2019-14
pp.69-74(NC), pp.91-96(IBISML)
CCS 2018-11-23
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
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