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A robustly stabilizing optimal control policy in a model-free mixed$\mathcal{H}_2/\mathcal{H}_\infty$-control setting is here put forward forcounterbalancing the slow convergence and non-robustness of traditionalhigh-variance policy optimization (and by extension policy gradient)algorithms. Leveraging It\^{o}'s stochastic differential calculus, weiteratively solve the system's continuous-time closed-loop generalizedalgebraic Riccati equation whilst updating its admissible controllers in atwo-player, zero-sum differential game setting. Our new results are illustratedby learning-enabled control systems which gather previously disseminatedresults in this field in one holistic data-driven presentation with greatersimplification, improvement, and clarity.

Mixed $\mathcal{H}_2/\mathcal{H}_\infty$-Policy Learning Synthesis