Abstract
Deep machine learning and reinforcement learning are two complementing fields within the study of intelligent systems. When combined, it is argued that they offer a promising path for achieving artificial general intelligence (AGI). This chapter outlines the concepts facilitating such merger of technologies and motivates a framework for building scalable intelligent machines. The prospect of utilizing custom neuromorphic devices to realize large-scale deep learning architectures is discussed, paving the way for achieving human level AGI.
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© 2012 ATLANTIS PRESS
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Arel, I. (2012). Deep Reinforcement Learning as Foundation for Artificial General Intelligence. In: Wang, P., Goertzel, B. (eds) Theoretical Foundations of Artificial General Intelligence. Atlantis Thinking Machines, vol 4. Atlantis Press, Paris. https://doi.org/10.2991/978-94-91216-62-6_6
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DOI: https://doi.org/10.2991/978-94-91216-62-6_6
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Publisher Name: Atlantis Press, Paris
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Online ISBN: 978-94-91216-62-6
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