Probabilistic Planning with Sequential Monte Carlo methodsDownload PDF

Published: 21 Dec 2018, Last Modified: 05 May 2023ICLR 2019 Conference Blind SubmissionReaders: Everyone
Abstract: In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal trajectories. This enables us to use sampling methods, and thus, tackle planning in continuous domains using a fixed computational budget. We design a new algorithm, Sequential Monte Carlo Planning, by leveraging classical methods in Sequential Monte Carlo and Bayesian smoothing in the context of control as inference. Furthermore, we show that Sequential Monte Carlo Planning can capture multimodal policies and can quickly learn continuous control tasks.
Keywords: control as inference, probabilistic planning, sequential monte carlo, model based reinforcement learning
TL;DR: Leveraging control as inference and Sequential Monte Carlo methods, we proposed a probabilistic planning algorithm.
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