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We present Learning to Drive (L2D), a low-cost benchmark for real-worldreinforcement learning (RL). L2D involves a simple and reproducibleexperimental setup where an RL agent has to learn to drive a Donkey car aroundthree miniature tracks, given only monocular image observations and speed ofthe car. The agent has to learn to drive from disengagements, which occurs whenit drives off the track. We present and open-source our training pipeline,which makes it straightforward to apply any existing RL algorithm to the taskof autonomous driving with a Donkey car. We test imitation learning,state-of-the-art model-free, and model-based algorithms on the proposed L2Dbenchmark. Our results show that existing RL algorithms can learn to drive thecar from scratch in less than five minutes of interaction. We demonstrate thatRL algorithms can learn from sparse and noisy disengagement to drive evenfaster than imitation learning and a human operator.

Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning