  Opportunistic spectrum access is one of the emerging techniques for
maximizing throughput in congested bands and is enabled by predicting idle
slots in spectrum. We propose a kernel-based reinforcement learning approach
coupled with a novel budget-constrained sparsification technique that
efficiently captures the environment to find the best channel access actions.
This approach allows learning and planning over the intrinsic state-action
space and extends well to large state spaces. We apply our methods to evaluate
coexistence of a reinforcement learning-based radio with a multi-channel
adversarial radio and a single-channel CSMA-CA radio. Numerical experiments
show the performance gains over carrier-sense systems.
