Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics
We propose a sampling-based framework for finite-horizon trajectory and policy optimization under differentiable dynamics by casting controller design as inference. Specifically, we minimize a KL-regularized expected trajectory cost, which yields an optimal "Boltzmann-tilted" distribution over controller parameters that concentrates on low-cost solutions as temperature decreases. To sample efficiently from this sharp, potentially multimodal target, we introduce tempered sequential Monte Carlo (TSMC): an annealing scheme that adaptively reweights and resamples particles along a tempering path from a prior to the target distribution, while using Hamiltonian Monte Carlo rejuvenation to maint...
| Authors | Heng Yang |
|---|---|
| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 9 May 2026 |
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