Skip to Main Content
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.
Autonomous Mental Development, IEEE Transactions on (Volume:2 , Issue: 2 )
Date of Publication: June 2010