The deployment of learning algorithms on autonomous vehicles is expensive, slow, and potentially unsafe. We present the F1/10 platform, a low-cost open-source 1/10th scale racecar and validated simulator which enables safe and rapid experimentation suitable for laboratory research settings. The F1/10 environment and hardware offer an accessible way to validate reinforcement learning policies learned in simulation and on a real car via the same intuitive OpenAI Gym API. To this effect, we demonstrate two approaches based on reinforcement learning that enable the transfer of policies from simulation to a real life track. The first demonstration details the use of our modular photorealistic simulator to learn an end-to-end racing policy in a self-supervised manner. Here, we demonstrate the F1/10’s capability for distributed online learning by sending batches of ‘experiences’ (video streams, odometry, etc.) back to a server, which asynchronously trains on this data and updates the racecar’s network weights on the fly. We also show a way to take quasi-deterministic ‘steps’ and perform ‘resets’ on the real car, thereby more closely mimicking the Gym API standards. The second demonstration uses our lightweight physics simulator to perform a joint optimization over a parameterized description of the racing trajectory, planning algorithms, and car dynamics, resulting in performance which exceeds all other entries in real-life races.