Learning to learn
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dopamine: generalization and bonuses
Neural Networks - Computational models of neuromodulation
Active learning with statistical models
Journal of Artificial Intelligence Research
Intrinsic Motivation Systems for Autonomous Mental Development
IEEE Transactions on Evolutionary Computation
AI in the 21st century - with historical reflections
50 years of artificial intelligence
Rudiments 1, 2 & 3: design speculations on autonomy
Proceedings of the fifth international conference on Tangible, embedded, and embodied interaction
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Children seem intrinsically motivated to manipulate, to explore, to test, to learn and they look for activities and situations that provide such learning opportunities. Inspired by research in developmental psychology and neuroscience, some researchers have started to address the problem of designing intrinsic motivation systems. A robot controlled by such systems is able to autonomously explore its environment not to fulfil predefined tasks but driven by an incentive to search for situations where learning happens efficiently. In this paper, we present the origins of these intrinsically motivated machines, our own research in this novel field and we argue that intrinsic motivation might be a crucial step towards machines capable of life-long learning and open-ended development.