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Probabilistic Planning with Sequential Monte Carlo Methods

Résumé

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.

Publication
International Conference on Learning Representations (ICLR)
Alexandre Piche
Alexandre Piche
Research Scientist

Research Scientist at AI Frontier Research located at Montreal, QC, Canada.

Christopher Pal
Christopher Pal
Distinguished Scientist

Distinguished Scientist at AI Research Partnerships & Ecosystem​ located at Montreal, QC, Canada.