ESE 618, Fall 2021 – Final Project
The final project structure and evaluation criteria are inspired by CS6789 at Cornell.
Important Dates
Tentative due dates:
Midterm report: 11/08
Final report due: 12/14
Grading
Project proposal: 5%
Midterm report: 10%
Final report due: 25%
Reports
Project proposal: Your proposal should be 2 pages maximum (not including references), and should include title, team members, abstract, related works, problem formulation and goals.
Midterm Report: Your report should be 4 pages maximum (not including references). Your midterm report should build on your project proposal, and outline your solution approach, current progress and preliminary results, as well as highlight challenges that you are facing.
Project Ideas
We provide a few project ideas below. Studying existing “L4DC” theory papers and reproducing proofs is also a good option for the course project. Numerical experiments used to verify conclusions or test conjectures are encouraged.
System identification for partially observed systems: Understand and survey how nonasymptotic guarantees for partially observed systems can be obtained, e.g., Simchowitz et al., Oymak & Ozay. Can you integrate these results methods from lowrank matrix recovery, e.g. Recht et al.?
Learning Kalman Filters from data: Conduct a survey on results characterizing the samplecomplexity and/or online regret incurred when learning Kalman Filters from data, e.g., Tsiamis & Pappas, Tsiamis & Pappas. Can you integrate these results with learning LQR results for bounds on LQG synthesis, e.g. Mania et al.?
System identification for sparse systems: Conduct a survey on results characterizing the samplecomplexity of learning linear systems that have additional structure (e.g., sparse A and B matrices, as in Fattahi et al., Bento et al.. Can you prove similar results for other types of useful structure? Can you extend these methods to scale favorably with system spectral radius?
Nontraditional control metrics: Conduct a survey and comparison of results on novel linear control metrics such as nonstochastic control e.g., Hazan et al., regretoptimal control, e.g., Goel & Hassibi, and competitive control, e.g., Goel & Hassibi, Li et al.. Can you cook up other interesting control metrics and solve for controllers that optimize for them?
Learning in constrained linear systems: Conduct a survey of results relating to learning to control constrained linear systems, e.g., Dean et al., Li et al.. Can you extend these results to classes of nonlinear systems?
Safe learning in nonlinear systems: Lyapunov and Barrier methods have proven to be effective tools in guaranteeing stability and safety for nonlinear control affine systems, e.g., Berkenkamp et al., Taylor et al.. Can you provide regret bounds for safe learning and control?
Contraction metrics in control: Conduct a survey on papers that use tools from contraction theory for the analysis/design of learning algorithms for nonlinear systems, e.g., Singh et al., Boffi et al., Sun et al., Tu et al.. Can you apply these ideas in other contexts?
Policy optimization methods: conduct a survey on results showing that direct policy optimization is effective for linear optimal control, e.g., Fazel et al., Mohammadi et al., Zheng et al., Zhang et al.. Can you extend these ideas to a class of nonlinear systems?
