Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering

arXiv:2403.20020v1 Announce Type: cross
Abstract: This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL). The proposed mappings benefit from the rich approximating properties of RKHSs, adopt no assumptions on the statistics of the data owing to their nonparametric nature, require no knowledge on transition probabilities of Markov decision processes, and may operate without any training data. Moreover, they allow for sampling on-the-fly via the design of trajectory samples, re-use past test data …

abstract application arxiv assumptions benefit cs.lg data designs eess.sp filtering kernel nature novel paper reinforcement reinforcement learning robust spaces statistics type