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On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator. (arXiv:2209.05042v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
The convergence of policy gradient algorithms in reinforcement learning
hinges on the optimization landscape of the underlying optimal control problem.
Theoretical insights into these algorithms can often be acquired from analyzing
those of linear quadratic control. However, most of the existing literature
only considers the optimization landscape for static full-state or output
feedback policies (controllers). We investigate the more challenging case of
dynamic output-feedback policies for linear quadratic regulation (abbreviated
as dLQR), which is prevalent in practice but has a …
arxiv case case study feedback landscape linear optimization regulator study