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Policy gradient methods, where one searches for the policy of interest bymaximizing the value functions using first-order information, becomeincreasingly popular for sequential decision making in reinforcement learning,games, and control. Guaranteeing the global optimality of policy gradientmethods, however, is highly nontrivial due to nonconcavity of the valuefunctions. In this exposition, we highlight recent progresses in understandingand developing policy gradient methods with global convergence guarantees,putting an emphasis on their finite-time convergence rates with regard tosalient problem parameters.

Global Convergence of Policy Gradient Methods in Reinforcement Learning, Games and Control