Solving mixed influence diagrams by reinforcement learning
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Date
2024-02-15
Authors
Prestwich, Steven D.
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Springer
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Abstract
While efficient optimisation methods exist for problems with special properties (linear, continuous, differentiable, unconstrained), real-world problems often involve inconvenient complications (constrained, discrete, multi-stage, multi-level, multi-objective). Each of these complications has spawned research areas in Artificial Intelligence and Operations Research, but few methods are available for hybrid problems. We describe a reinforcement learning-based solver for a broad class of discrete problems that we call Mixed Influence Diagrams, which may have multiple stages, multiple agents, multiple non-linear objectives, correlated chance variables, exogenous and endogenous uncertainty, constraints (hard, soft and chance) and partially observed variables. We apply the solver to problems taken from stochastic programming, chance-constrained programming, limited-memory influence diagrams, multi-level and multi-objective optimisation. We expect the approach to be useful on new hybrid problems for which no specialised solution methods exist.
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Keywords
Artificial Intelligence (AI) , Optimisation , Reinforcement learning , Mixed Influence Diagrams , Operations research
Citation
Prestwich, S.D. (2024) ‘Solving mixed influence diagrams by reinforcement learning’, in G. Nicosia, V. Ojha, E. La Malfa, G. La Malfa, P.M. Pardalos, and R. Umeton (eds) Machine Learning, Optimization, and Data Science, LOD 2023. Lecture Notes in Computer Science, vol 14506. Springer, Cham, pp. 255–269. https://doi.org/10.1007/978-3-031-53966-4_19
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© the authors 2024. This is a post-peer-review, pre-copyedit version of a paper published as: Prestwich, S.D. (2024). Solving Mixed Influence Diagrams by Reinforcement Learning, LOD 2023. Lecture Notes in Computer Science, vol 14506. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-53966-4_19