We introduce a new reinforcement learning benchmark based on the classic platform game Super Mario Bros. The benchmark has a high-dimensional input space, and achieving a good score requires sophisticated and varied strategies. However, it has tunable difficulty, and at the lowest difficulty setting decent score can be achieved using rudimentary strategies and a small fraction of the input space. To investigate the properties of the benchmark, we evolve neural network-based controllers using different network architectures and input spaces. We show that it is relatively easy to learn basic strategies capable of clearing individual levels of low difficulty, but that these controllers have problems with generalization to unseen levels and with taking larger parts of the input space into account. A number of directions worth exploring for learning better-performing strategies are discussed.