77 citations · 189 across the 16 of their papers we have counts for
4 papers · 1 filter
Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming
Tadashi Kozuno, Eiji Uchibe, Kenji Doya
Approximate dynamic programming algorithms, such as approximate value iteration, have been successfully applied to many complex reinforcement learning tasks, and a better approxima…
Connectivity Inference from Neural Recording Data: Challenges, Mathematical Bases and Research Directions
Ildefons Magrans de Abril, Junichiro Yoshimoto, Kenji Doya
This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We fir…
Online Meta-learning by Parallel Algorithm Competition
Stefan Elfwing, Eiji Uchibe, Kenji Doya
The efficiency of reinforcement learning algorithms depends critically on a few meta-parameters that modulates the learning updates and the trade-off between exploration and exploi…
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
Stefan Elfwing, Eiji Uchibe, Kenji Doya
In recent years, neural networks have enjoyed a renaissance as function approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon achieved near top-level human…