The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) will be held February 4–9 at the Hilton San Francisco, San Francisco, California, USA. The following oral / poster presentation will be delivered.
Maximum entropy inverse reinforcement learning (MaxEnt IRL) is an effective approach for learning the underlying rewards of demonstrated human behavior, while it is intractable in high-dimensional state space due to the exponential growth of calculation cost. In recent years, a few works on approximating MaxEnt IRL in large state spaces by graphs provide successful results, however, types of state space models are quite limited.
In this work, we extend them to more generic large state space models with graphs where time interval consistency of Markov decision processes is guaranteed. We validate our proposed method in the context of driving behavior prediction. Experimental results using actual driving data confirm the superiority of our algorithm in both prediction performance and computational cost over other existing IRL frameworks.
— Presentation information —
Technical session “Planning” 2017-02-06 (Mon.) 14:00 – 15:30
Fast Inverse Reinforcement Learning with Interval Consistent Graph for Driving Behavior Prediction
Masamichi Shimosaka*, Junichi Sato**, Kazuhito Takenaka***, Kentarou Hitomi***
(*Tokyo Institute of Technology, **The University of Tokyo, ***DENSO CORPORATION)
Poster / Demo Session 1 2017-02-06 (Mon.) 18:30 – 20:30