Sequential Monte Carlo for Policy Optimization in Continuous POMDPs

Hany Abdulsamad, Sahel Iqbal, Simo Särkkä

Keywords: POMDP, Policy Optimization, Sequential Monte Carlo, Feynman--Kac, Control-as-Inference, Decision-Making under Uncertainty

TL;DR: We solve POMDPs by nesting sequential Monte Carlo

Abstract: 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.

Primary Area: Reinforcement learning (e.g., decision and control, planning, hierarchical RL, robotics)

Submission Number: 13492