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High-level behavior control of an e-pet with reinforcement learning

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2 Author(s)
Chih-Wei Hsu ; MeeGo Group, Inst. for Inf. Ind., Tainan, Taiwan ; Liu, A.

One of attractive features of electronic-pets (e-pets) is interaction between the user and the e-pet. The interaction, however, is usually limited to using the predefined commands. In this paper, we present a way of involving the user in helping an e-pet learn high-level behaviors based on basic actions. The high-level behaviors are derived with planning, and the execution of the behaviors is then trained with reinforcement learning. In this research, we explain how we use a partially observable Markov decision process and the hierarchical task network planning for designing behaviors. A Q-learning method is then applied to the training of the e-pet for achieving the correct behavior. A prototype is presented to show its feasibility and effectiveness.

Published in:

Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on

Date of Conference:

10-13 Oct. 2010

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