By Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu
The 4 quantity set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitutes the court cases of the twenty third foreign convention on Neural details Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 complete papers offered have been conscientiously reviewed and chosen from 431 submissions. The four volumes are equipped in topical sections on deep and reinforcement studying; immense facts research; neural information research; robotics and keep an eye on; bio-inspired/energy effective info processing; entire mind structure; neurodynamics; bioinformatics; biomedical engineering; information mining and cybersecurity workshop; desktop studying; neuromorphic undefined; sensory notion; trend popularity; social networks; brain-machine interface; desktop imaginative and prescient; time sequence research; data-driven procedure for extracting latent gains; topological and graph dependent clustering equipment; computational intelligence; info mining; deep neural networks; computational and cognitive neurosciences; idea and algorithms.
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Additional info for Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part I
Namely, the hidden layers are commonly used by both actor and critic. In the learning of the critic, Temporal Diﬀerence (TD) error rˆt is used rˆt = rt + γP (st ) − P (st−1 ) (1) where rt is a reward, γ is a discount factor, st is a state vector, and P (st ) denotes a state value. The state value at t − 1, P (st−1 ) is trained by P T (st−1 ) = P (st−1 ) + rˆt = rt + γP (st ) (2) where P T (st−1 ) denotes the training signal for the state value. On the other hand, the actor output vector a(st−1 ) is trained by aT (st−1 ) = a(st−1 ) + rˆt rndt−1 (3) 34 K.
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