Sequential Monte Carlo for Policy Optimization in Continuous POMDPs

Hany Abdulsamad (University of Amsterdam) · Sahel Mohammad Iqbal (Aalto University) · Simo Sarkka (Aalto University)

algorithm effectivenesscontinuous partially observable markov decision processescontinuous pomdp benchmarksexploration-exploitation trade-off handcrafted heuristicshistory-dependent policy gradientnested sequential monte carlo algorithmnon-markovian feynman-kac modeloptimal decision-makingoptimal trajectory distributionpartial observabilitypolicy optimization frameworkprobabilistic inferencesuboptimal approximationsuncertainty managementvalue of information gathering

Optimal decision-making under partial observability requires agents to balance reducing uncertainty (exploration) against pursuing immediate objectives (exploitation). In this paper, we introduce a novel policy optimization framework for continuous partially observable Markov decision processes (POMDPs) that explicitly addresses this challenge. Our method casts policy learning as probabilistic inference in a non-Markovian Feynman--Kac model that inherently captures the value of information gathering by anticipating future observations, without requiring suboptimal approximations or handcrafted heuristics. To optimize policies under this model, we develop a nested sequential Monte Carlo (SMC) algorithm that efficiently estimates a history-dependent policy gradient under samples from the optimal trajectory distribution induced by the POMDP. We demonstrate the effectiveness of our algorithm across standard continuous POMDP benchmarks, where existing methods struggle to act under uncertainty.