The radio-frequency spectrum is a precious resource, with many applications and users, especially with the recent spectrum auction in the United States. Future platforms and devices, such as radars and radios, need to be adaptive to their spectral environment in order to continue serving the needs of their users. This thesis considers an environment with one tracking radar, a single target, and a communications system. The radar-communications coexistence problem is modeled as a Markov decision process (MDP), and reinforcement learning is applied to drive the radar to optimal behavior. / Master of Science / The radio-frequency electromagnetic spectrum is a precious resource, in which users and operators are assigned frequency slots in which they can operate. The federal spectrum auction in the United States freed up some of the spectrum for shared use. The implications of this are the spectrum will become more dense; there will be more devices and users in the same amount of spectrum. The devices and platforms of this spectrum need to be more adaptive and agile in order to (1) not be interfered by other systems, (2) cause interference to other systems, and (3) continue to meet the needs of users (e.g. cell phone users) and operators (e.g. military radar). The work presented in this thesis applies Markov decision process and reinforcement learning to solve the problem.