Many product innovations can benefit from being more adaptive to their environment. Learning adaptive behavior can be time sensitive and complex. For that purpose we created a simulation environment with a reinforcement learning component for developing simple adaptive products. The environment models a persona and a physical product and the reinforcement learning algorithm learns how the physical product should respond to the persona. The trained model can then be used in the software layer of the physical product to make it adaptive.