This paper presents the results of first ever implementation of learning algorithm for opportunistic spectrum access (OSA) in lab conditions. The OSA scheme consists of two USRP N210 platforms, one acting as primary users of a primary network and another as secondary user. Primary user network generates carriers with a pre-defined probability of occupancy through an OFDM modulation scheme implemented in GRC environment (GNU Radio Companion). Secondary user, implemented using another USRP platform programmed through SimulinkTM environment, learns and predicts the channel's (carriers) occupancy with the help of learning algorithms. Two reinforcement learning algorithms, UCB (Upper Confidence Bound) and WD (Weight Driven), are used by a secondary user to learn and predict the spectrum occupancy. These learning algorithms have been chosen as they are capable of acting and learning in highly unpredictable conditions such as met in cognitive radio context. This proof-ofconcept validates decision making capabilities of reinforcement learning algorithms for OSA in real wireless conditions. At the end, performance comparison between these two learning algorithms in is also done.