Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
This addresses the challenge of decision-making under uncertainty in continuous POMDPs, which is a domain-specific problem for robotics or control systems, and it appears to be a novel method rather than incremental.
The paper tackles policy optimization in continuous partially observable Markov decision processes (POMDPs) by introducing a novel framework that casts learning as probabilistic inference, and it demonstrates effectiveness on benchmarks where existing methods struggle.
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.