Adaptive Online Value Function Approximation with Wavelets. (arXiv:2204.11842v1 [cs.LG])

Using function approximation to represent a value function is necessary for
continuous and high-dimensional state spaces. Linear function approximation has
desirable theoretical guarantees and often requires less compute and samples
than neural networks, but most approaches suffer from an exponential growth in
the number of functions as the dimensionality of the state space increases. In
this work, we introduce the wavelet basis for reinforcement learning. Wavelets
can effectively be used as a fixed basis and additionally provide the ability
to …

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