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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.

[1]Abbeel, P., Ng, A.Y., 2004. Apprenticeship Learning via Inverse Reinforcement Learning. Proc. 21st Int. Conf. on Machine Learning, p.1-8.

[2]Abbeel, P., Ng, A.Y., 2005. Exploration and Apprenticeship Learning in Reinforcement Learning. Proc. 22nd Int. Conf. on Machine Learning, p.1-8.

[3]Abbeel, P., Dolgov, D., Ng, A.Y., Thrun, S., 2008. Apprenticeship Learning for Motion Planning with Application to Parking Lot Navigation. Proc. Int. Conf. on Intelligent Robots and Systems, p.1083-1090.

[4]Amit, R., Mataric, M., 2002. Learning Movement Sequences from Demonstration. Proc. 2nd Int. Conf. on Development and Learning, p.203-208.

[5]Atkeson, C., Schaal, S., 1997. Robot Learning from Demonstration. Proc. 14th Int. Conf. on Machine Learning, p.12-20.

[6]Coates, A., Abbeel, P., Ng, A.Y., 2009. Apprenticeship learning for helicopter control. Commun. ACM, 52(7):97-105.

[7]Hayes, G., Demiris, J., 1994. A Robot Controller Using Learning by Imitation. Proc. 2nd Int. Symp. on Intelligent Robotic Systems, p.198-204.

[8]Kolter, J.Z., Abbeel, P., Ng, A.Y., 2008a. Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion. Advances in Neural Information Processing Systems. MIT Press, Cambridge, p.769-776.

[9]Kolter, J.Z., Rodgers, M.P., Ng, A.Y., 2008b. A Complete Control Architecture for Quadruped Locomotion over Rough Terrain. Proc. Int. Conf. on Robotics and Automation, p.811-818.

[10]Kuniyoshi, Y., Inaba, M., Inoue, H., 1994. Learning by watching: extracting reusable task knowledge from visual observation of human performance. IEEE Trans. Rob. Autom., 10(6):799-822.

[11]Mitchell, T., 1997. Machine Learning. McGraw Hill, New York, p.385-392.

[12]Ng, A.Y., Russell, S., 2000. Algorithms for Inverse Reinforcement Learning. Proc.17th Int. Conf. on Machine Learning, p.663-670.

[13]Ng, A.Y., Harada, D., Russell, S., 1999. Policy Invariance under Reward Transformations: Theory and Application to Reward Shaping. Proc. 16th Int. Conf. on Machine Learning, p.278-287.

[14]Pomerleau, D., 1989. Alvinn: an Autonomous Land Vehicle in a Neural Network. Advances in Neural Information Processing Systems 1. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, p.305-313.

[15]Puterman, M., 1994. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, New York, NY.

[16]Rebula, J.R., Neuhaus, P.D., Bonnlander, B.V., Johnson, M.J., Pratt, J.E., 2007. A Controller for the LittleDog Quadruped Walking on Rough Terrain. IEEE Int. Conf. on Robotics and Automation, p.1467-1473.

[17]Russell, S., 1998. Learning Agents for Uncertain Environments. Proc. 11th Annual Conf. on Computational Learning Theory, p.101-103.

[18]Sammut, C., Hurst, S., Kedzier, D., Michie, D., 1992. Learning to Fly. Proc. 9th Int. Workshop on Machine Learning, p.385-393.

[19]Sutton, R.S., Barto, A.G., 1998. Reinforcement Learning. MIT Press, USA.