Database Selection is the problem of choosing, from a finite number of databases, the one that contains the most relevant information pertaining to a query. Previous approaches to this problem consisted of deterministic search techniques in conjunction with efficient pruning of search spaces, based on vector space model and ranking. In this article, we propose a new probabilistic search method, based on a reinforcement learning algorithm, to solve the database selection problem, where an agent learns to map situations to action by means of receiving reward and penalties for the action taken and trying to maximize its rewards. We use reinforcement algorithm with user feedback to learn a policy, which maps a particular topic or particular interest to a set of databases. Reinforcement learning is an attractive approach to this problem due to its ability to generate optimal solutions for both stationary and non-stationary/dynamically changing environments(queries). Experiments with simple queries show that reinforcement learning has potential to be considered as an efficient approach for database selection.