Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering
Yuki Akiyama,
Minh Vu,
Konstantinos Slavakis
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 pa… Show more
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