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- W136985510 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.
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- W136985510 date "2011-05-02" @default.
- W136985510 modified "2023-09-23" @default.
- W136985510 title "Escaping local optima in POMDP planning as inference" @default.
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- W136985510 doi "https://doi.org/10.5555/2034396.2034517" @default.
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