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Traditional autonomous vehicle pipelines that follow a modular approach havebeen very successful in the past both in academia and industry, which has ledto autonomy deployed on road. Though this approach provides ease ofinterpretation, its generalizability to unseen environments is limited andhand-engineering of numerous parameters is required, especially in theprediction and planning systems. Recently, deep reinforcement learning has beenshown to learn complex strategic games and perform challenging robotic tasks,which provides an appealing framework for learning to drive. In this work, wepropose a deep reinforcement learning framework to learn optimal control policyusing waypoints and low-dimensional visual representations, also known asaffordances. We demonstrate that our agents when trained from scratch learn thetasks of lane-following, driving around inter-sections as well as stopping infront of other actors or traffic lights even in the dense traffic setting. Wenote that our method achieves comparable or better performance than thebaseline methods on the original and NoCrash benchmarks on the CARLA simulator.

Affordance-based Reinforcement Learning for Urban Driving