3 citations · 3 across the 5 of their papers we have counts for
5 papers
Nonparametric Bellman Mappings for Value Iteration in Distributed Reinforcement Learning
Yuki Akiyama, Konstantinos Slavakis
This paper introduces novel Bellman mappings (B-Maps) for value iteration (VI) in distributed reinforcement learning (DRL), where agents are deployed over an undirected, connected…
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering
Yuki Akiyama, Minh Vu, Konstantinos Slavakis
This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL). The proposed mappings benefit from the rich ap…
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering
Yuki Akiyama, Konstantinos Slavakis
This paper aims at the algorithmic/theoretical core of reinforcement learning (RL) by introducing the novel class of proximal Bellman mappings. These mappings are defined in reprod…
online and lightweight kernel-based approximated policy iteration for dynamic p-norm linear adaptive filtering
Yuki Akiyama, Minh Vu, Konstantinos Slavakis
This paper introduces a solution to the problem of selecting dynamically (online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on th…
Dynamic selection of p-norm in linear adaptive filtering via online kernel-based reinforcement learning
Minh Vu, Yuki Akiyama, Konstantinos Slavakis
This study addresses the problem of selecting dynamically, at each time instance, the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on th…