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A Comparative Study of Value Systems for Self-Motivated Exploration and Learning by Robots

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1 Author(s)
Merrick, K.E. ; Univ. of New South Wales at ADFA, Canberra, ACT, Australia

A range of different value systems have been proposed for self-motivated agents, including biologically and cognitively inspired approaches. Likewise, these value systems have been integrated with different behavioral systems including reflexive architectures, reward-based learning and supervised learning. However, there is little literature comparing the performance of different value systems for motivating exploration and learning by robots. This paper proposes a neural network architecture for integrating different value systems with reinforcement learning. It then presents an empirical evaluation and comparison of four value systems for motivating exploration by a Lego Mindstorms NXT robot. Results reveal the different exploratory properties of novelty-seeking motivation, interest and competence-seeking motivation.

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Autonomous Mental Development, IEEE Transactions on  (Volume:2 ,  Issue: 2 )