Autonomous systems that operate in continuous state, action, and observation spaces require planning and reasoning under uncertainty. Existing online planning methods for such POMDPs are almost exclusively sample-based, yet they forego the power of high-dimensional gradient optimization as combining it into Monte Carlo Tree Search (MCTS) has proved difficult, especially in non-parametric settings. We close this gap with three contributions. First, we derive a novel action-gradient theorem for both MDPs and POMDPs in terms of transition likelihoods, making gradient information accessible during tree search. Second, we introduce the Multiple Importance Sampling (MIS) tree, that re-uses samples for changing action branches, yielding consistent value estimates that enable in-search gradient steps. Third, we derive exact transition probability computation via the area formula for smooth generative models common in physical domains, a result of independent interest. These elements combine into Action-Gradient Monte Carlo Tree Search (AGMCTS), the first planner to blend non-parametric particle search with online gradient refinement in POMDPs. Across several challenging continuous MDP and POMDP benchmarks, AGMCTS outperforms widely-used sample-only solvers in solution quality.
Lev-Yehudi et al. (Sat,) studied this question.