We consider a hierarchical spectrum sharing network consisting of a primary and a cognitive secondary transmitter-receiver pair, with non-backlogged traffic. The secondary transmitter may utilize cooperative transmission techniques to relay primary traffic while superimposing its own information, or transmit opportunistically when the primary user is idle. The secondary user meets a dilemma in this scenario. Choosing cooperation it can transmit a packet immediately even if the primary queue is not empty, but it has to bear the additional cost of relaying, since the primary performance needs to be guaranteed. To solve this dilemma we propose dynamic cooperative secondary access control that takes the state of the spectrum sharing network into account. We formulate the problem as a Markov Decision Process (MDP) and prove the existence of a stationary policy that is average cost optimal. Then we consider the scenario when the traffic and link statistics are not known at the secondary user, and propose to find the optimal transmission strategy using reinforcement learning. With extensive numerical evaluation, we demonstrate that dynamic cooperation with state aware sequential decision is very efficient in spectrum sharing systems with stochastic traffic, and show that dynamic cooperation is necessary for the secondary system to be able to adapt to changing load conditions or to changing available energy resource. Our results show, that learning based access control, with or without known primary buffer state, has close to optimal performance. / <p>QS 2013</p>