2 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 2 cited
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…
stat.ML2017★ 2 cited
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…