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Tue, July 8, 2025
Policy Optimization methods enjoy wide practical use in Reinforcement Learning (RL) for applications ranging from robotic manipulation to game-playing, partly because they are easy to implement, and require only black-box access to the underlying model. This talk focuses on recent developments in policy optimization (a gradient-based approach for feedback control design) popularized by its success in RL. We describe theoretical results on the optimization landscape, global convergence, and sample complexity of policy optimization in several canonical continuous control problems, despite their nonconvexity in policy parameters, by exploiting structural properties such as the Polyak-Lojasiewicz (or gradient dominance) condition. This line of work attempts to bring control theory and RL closer, and has helped advance two recent trends: (1) use of control problems as benchmarks for less-understood RL algorithms, and (2) theoretically-sound use of RL-style methods in control.