Inverse Reinforcement Learning

Using inverse reinforcement learning to infer human preferences is challenging, because it is an underspecified problem. We use multi-task RL pre-training and successor features to learn a strong prior over the space of reasonable goals in an environment—which we call a basis—that enables rapidly inferring an expert’s reward function in only 100 samples.