Convex-Concave Reinforcement Learning
Policy learning drives many of the most consequential and heavily-invested applications of reinforcement learning today. Yet the core optimization problem it rests on (maximizing expected return) is notoriously non-convex, even under a direct policy parameterization, and the field has largely responded by avoiding it:...
Shripad Deshmukh, Yaswanth Chittepu, Dhawal Gupta et al.
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