A connection has recently been drawn between dynamic optimization and reinforcement learning problems as subsets of a broader class of sequential decision-making problems. We present a unified approach that enables the cross-pollination of ideas between established communities, and could help to develop rigorous methods for algorithm comparison and selection for real-world resource-constrained problems.
CITATION STYLE
Soni, A., Lewis, P. R., & Ekárt, A. (2018). Synergies between reinforcement learning and evolutionary dynamic optimisation. In Communications in Computer and Information Science (Vol. 732, pp. 91–96). Springer Verlag. https://doi.org/10.1007/978-3-319-90418-4_7
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