Adaptive importance sampling for control and inference
This work addresses the largely unsolved problem of efficient control and inference in non-linear stochastic systems, with applications in fields like neuroscience for estimating connectivity from neural data, though it appears incremental as it builds on existing path integral control theory.
The paper tackles the challenge of learning and representing state-feedback controllers for non-linear stochastic control problems by introducing the Path Integral Cross Entropy (PICE) method, a gradient descent approach that uses ideas from the cross entropy method to learn controllers with arbitrary parametrization, and demonstrates its application in simple examples and as an accurate alternative to particle filtering for posterior estimation in latent state models.
Path integral (PI) control problems are a restricted class of non-linear control problems that can be solved formally as a Feyman-Kac path integral and can be estimated using Monte Carlo sampling. In this contribution we review path integral control theory in the finite horizon case. We subsequently focus on the problem how to compute and represent control solutions. Within the PI theory, the question of how to compute becomes the question of importance sampling. Efficient importance samplers are state feedback controllers and the use of these requires an efficient representation. Learning and representing effective state-feedback controllers for non-linear stochastic control problems is a very challenging, and largely unsolved, problem. We show how to learn and represent such controllers using ideas from the cross entropy method. We derive a gradient descent method that allows to learn feed-back controllers using an arbitrary parametrisation. We refer to this method as the Path Integral Cross Entropy method or PICE. We illustrate this method for some simple examples. The path integral control methods can be used to estimate the posterior distribution in latent state models. In neuroscience these problems arise when estimating connectivity from neural recording data using EM. We demonstrate the path integral control method as an accurate alternative to particle filtering.