Model-Based Reinforcement Learning via Stochastic Hybrid Models
This work addresses the problem of limited interpretability in data-driven control methods for automation, offering a structured approach to nonlinear modeling and control.
The paper tackles the challenge of optimal control in nonlinear systems by introducing a hybrid-system view that decomposes complex dynamics into simpler localized units, resulting in a novel hybrid relative entropy policy search (Hb-REPS) technique that optimizes piecewise feedback controllers derived from a piecewise polynomial approximation of a global state-value function.
Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently successfully tackled challenging applications. However, such methods often obscure the structure of dynamics and control behind black-box over-parameterized representations, thus limiting our ability to understand closed-loop behavior. This paper adopts a hybrid-system view of nonlinear modeling and control that lends an explicit hierarchical structure to the problem and breaks down complex dynamics into simpler localized units. We consider a sequence modeling paradigm that captures the temporal structure of the data and derive an expectation-maximization (EM) algorithm that automatically decomposes nonlinear dynamics into stochastic piecewise affine models with nonlinear transition boundaries. Furthermore, we show that these time-series models naturally admit a closed-loop extension that we use to extract local polynomial feedback controllers from nonlinear experts via behavioral cloning. Finally, we introduce a novel hybrid relative entropy policy search (Hb-REPS) technique that incorporates the hierarchical nature of hybrid models and optimizes a set of time-invariant piecewise feedback controllers derived from a piecewise polynomial approximation of a global state-value function.