Deep Reinforcement Learning for Beyond-Conceivable Forecasting
Contact person: Glenn Kristiansen
Keywords: Deep Learning, Reinforcement Learning, Forecasting, Digital Technology, Economics
Research group: Entrepreneurship (ENT)
Department of Informatics
We seek to determine to what extent deep reinforcement learning (DRL) could help regulatory- and corporate decision makers in imagining possible, but not immediately obvious, distant future development and application of digital technologies outlined within regulatory sandboxes. Is it possible for DRL to show currently unknown pathways toward such a discontinuous future? Such pathways would need to be modelled by combing domain knowledge in digital technology, governance mechanisms, and economics. DRL is a machine learning approach allowing for meta learning, meaning that algorithms potentially can self-learn from experience. Implementation of DRL within digital-economic environments is relatively new, with significant promise since the algorithms interact directly with its novel environment. The question raises itself why we would need DRL in the first place? Our ability to imagine alternative future pathways is highly bound by our present everyday lives and previous experiences, which may not represent anything like the outcome from Industry 4.0.
We seek a postdoc who