CaRL: Learning Scalable Planning Policies with Simple Rewards

We investigate reinforcement learning (RL) for privileged planning in autonomous driving. State-of-the-art approaches for this task are rule-based, but these methods do not scale to the long tail. RL, on the other hand, is scalable and does not suffer from compounding errors like imitation learning. Contemporary RL approaches for driving use complex shaped rewards that sum multiple individual rewards, e.g. progress, position, or orientation rewards. We show that PPO fails to optimize a popular..