3 citations · 9 across the 4 of their papers we have counts for
4 papers
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning
Tadashi Kozuno, Dongqi Han, Kenji Doya
In real-world applications of reinforcement learning (RL), noise from inherent stochasticity of environments is inevitable. However, current policy evaluation algorithms, which pla…
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…