Scaling Curriculum Learning For Autonomous Driving
CL4AD is presented, the first integration of curriculum learning into batched autonomous driving simulators by framing scenario selection as an unsupervised environment design problem, and utility functions that shape curricula based on success rates and the realism of the agent's behavior are introduced, in addition to existing regret-estimation functions.