  We consider the dynamic multichannel access problem, which can be formulated
as a partially observable Markov decision process (POMDP). We first propose a
model-free actor-critic deep reinforcement learning based framework to explore
the sensing policy. To evaluate the performance of the proposed sensing policy
and the framework's tolerance against uncertainty, we test the framework in
scenarios with different channel switching patterns and consider different
switching probabilities. Then, we consider a time-varying environment to
identify the adaptive ability of the proposed framework. Additionally, we
provide comparisons with the Deep-Q network (DQN) based framework proposed in
[1], in terms of both average reward and the time efficiency.
