A possibility for implementing curiosity and boredom in model-building neural controllers
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
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One of the striking differences between current reinforcement learning algorithms and early human learning is that animals and infants appear to explore their environments with autonomous purpose, in a manner appropriate to their current level of skills. An important intuition for autonomously motivated exploration was proposed by Schmidhuber [1,2]: an agent should be interested in making observations that reduce its uncertainty about future observations. However, there is not yet a theoretical analysis of the usefulness of autonomous exploration in respect to the overall performance of a learning agent. We discuss models for a learning agent's autonomous exploration and present some recent results. In particular, we investigate the exploration time for navigating effectively in a Markov Decsion Process (MDP) without rewards, and we consider extensions to MDPs with infinite state spaces.