We improve inverse reinforcement learning (IRL) by applying dimension reduction methods to automatically extract abstract features from human-demonstrated policies

to deal with the cases where features are either unknown or numerous. The importance rating of each abstract feature is incorporated into the reward function. Simulation is performed on a task of driving in a five-lane highway

where the controlled car has the largest fixed speed among all the cars. Performance is almost 10.6% better on average with than without importance ratings.