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- W2292526794 abstract "Planning as inference recently emerged as a versatile approach to decision-theoretic planning and reinforcement learning for single and multi-agent systems in fully and partially observable domains with discrete and continuous variables. Since planning as inference essentially tackles a non-convex optimization problem when the states are partially observable, there is a need to develop techniques that can robustly escape local optima. We propose two algorithms: the first one adds nodes to the controller according to an increasingly deep forward search, while the second one splits nodes in a greedy fashion to improve reward likelihood." @default.
- W2292526794 created "2016-06-24" @default.
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- W2292526794 date "2011-01-01" @default.
- W2292526794 modified "2023-09-27" @default.
- W2292526794 title "Escaping Local Optima in POMDP Planning as Inference (Extended Abstract)" @default.
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