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Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] 2020-06-29
Online Online A Study on Model-based Deep Reinforcement Learning Using Autonomous Search for Subgoal
Motoki Maruyama, Satoshi Endo, Koji Yamada (Univ. of the Ryukyus) NC2020-6 IBISML2020-6
Model-based deep reinforcement learning (DRL) is more sample-efficient than model-free DRL. But it requires a deep gener... [more] NC2020-6 IBISML2020-6
IBISML 2013-03-04
Aichi Nagoya Institute of Technology Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation
Syogo Mori, Voot Tangkaratt, Tingting Zhao (Tokyo Inst. of Tech.), Jun Morimoto (ATR), Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2012-95
(To be available after the conference date) [more] IBISML2012-95
Miyagi Tohoku Univ. Task-dependent selection of model-free and model-based strategies
Akihiro Funamizu (Univ. Tokyo), Makoto Ito, Kenji Doya (OIST), Ryohei Kanzaki (Univ. Tokyo), Hirokazu Takahashi (Univ. of Tokyo/PRESTO JST) NC2009-57
To investigate the roles of model-free and model-based strategies in action learning, we examined how well the two behav... [more] NC2009-57
NC, NLP 2009-07-13
Nara NAIST Composition of Feature Space and State Space Dynamics Models for Model-based Reinforcement Learning
Akihiko Yamaguchi, Jun Takamatsu, Tsukasa Ogasawara (NAIST) NLP2009-15 NC2009-8
Learning a dynamics model and a reward model during reinforcement learning is a useful way, since the agent can also upd... [more] NLP2009-15 NC2009-8
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