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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2016-11-17
14:00		 Kyoto Kyoto Univ. Incremental Natural Actor Critic with Importance Weight Aware Update
Ryo Iwaki (Osaka Univ.), Hiroki Yokoyama (Tamagawa Univ.), Minoru Asada (Osaka Univ.) IBISML2016-81		 Appropriate tuning of step-size parameter is crucial for reinforcement learning, as well as other machine learning techn... [more] IBISML2016-81
pp.251-257
IBISML 2011-11-09
15:45		 Nara Nara Womens Univ. Modified Newton Approach to Policy Search
Hirotaka Hachiya (Tokyo Inst. of Tech.), Tetsuro Morimura (IBM Japan), Takaki Makino (Univ. of Tokyo), Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-54		 The natural policy gradient method was shown to be a useful approach to policy search in reinforcement learning. However... [more] IBISML2011-54
pp.79-85
MBE, NC
(Joint)		 2007-12-22
16:10		 Aichi   The Policy Gradient On The Average Reward Manifold
Takamitsu Matsubara (NAIST/ATR), Jun Morimoto (JST/ATR) NC2007-85		 In this paper we propose a novel policy gradient type reinforcement learning method on the average rewardmanifold, in wh... [more] NC2007-85
pp.81-86
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