2024
Proximal Bellman Mappings for Reinforcement Learning and Their Application to Robust Adaptive Filtering
ICASSP 2024accepted
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 reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approximation properties and inner product of RKHSs, they…