Unbounded Output Networks for Classification

no code implementations • 25 Jul 2018 • Stefan Elfwing, Eiji Uchibe, Kenji Doya

In this study, by adopting features of the EE-RBM approach to feed-forward neural networks, we propose the UnBounded output network (UBnet) which is characterized by three features: (1) unbounded output units; (2) the target value of correct classification is set to a value much greater than one; and (3) the models are trained by a modified mean-squared error objective.

Classification General Classification

Online Meta-learning by Parallel Algorithm Competition

no code implementations • 24 Feb 2017 • Stefan Elfwing, Eiji Uchibe, Kenji Doya

In the OMPAC method, several instances of a reinforcement learning algorithm are run in parallel with small differences in the initial values of the meta-parameters.

Atari Games Meta-Learning +3

Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning

no code implementations • 10 Feb 2017 • Stefan Elfwing, Eiji Uchibe, Kenji Doya

First, we propose two activation functions for neural network function approximation in reinforcement learning: the sigmoid-weighted linear unit (SiLU) and its derivative function (dSiLU).

Atari Games reinforcement-learning +1