CIMRL: Combining Imitation and Reinforcement Learning for Safe Autonomous Driving

In the realm of Autonomous Driving (AD), two major approaches have been applied for decades: Imitation Learning (IL) and Reinforcement Learning (RL). Both have their pros and cons: On one hand, IL is an effective algorithm using supervised learning which scales with data but is prone to accumulated error in “out-of-distribution” events. On the other hand, RL is designed to be adaptable in unseen scenarios but can be very challenging when it comes to obtaining human-like behavior (i.e., it is hard to define a reward function).