When is Particle Filtering Efficient for POMDP Sequential Planning?

10 Jun 2020Simon S. DuWei HuZhiyuan LiRuoqi ShenZhao SongJiajun Wu

Particle filtering is a popular method for inferring latent states in stochastic dynamical systems, whose theoretical properties have been well studied in machine learning and statistics communities. In sequential decision-making problems, e.g., partially observed Markov decision processes (POMDPs), oftentimes the inferred latent state is further used for planning at each step... (read more)

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