Abstract: 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.

Key words: Importance rating, Abstract feature, Feature extraction, Inverse reinforcement learning (IRL), Markov decision process (MDP)

Please provide your name, email address and a comment

Your Name: Affili: E-Mail Address:

Comment (Please comment in English):


Verification Code(click to change a pic):