  In this paper, we consider the multichannel rendezvous problem in cognitive
radio networks (CRNs) where the probability that two users hopping on the same
channel have a successful rendezvous is a function of channel states. The
channel states are modelled by two-state Markov chains that have a good state
and a bad state. These channel states are not observable by the users. For such
a multichannel rendezvous problem, we are interested in finding the optimal
policy to minimize the expected time-to-rendezvous (ETTR) among the class of
{\em dynamic blind rendezvous policies}, i.e., at the $t^{th}$ time slot each
user selects channel $i$ independently with probability $p_i(t)$, $i=1,2,
\ldots, N$. By formulating such a multichannel rendezvous problem as an
adversarial bandit problem, we propose using a reinforcement learning approach
to learn the channel selection probabilities $p_i(t)$, $i=1,2, \ldots, N$. Our
experimental results show that the reinforcement learning approach is very
effective and yields comparable ETTRs when comparing to various approximation
policies in the literature.
