Approximation of Convex Envelope Using Reinforcement Learning.
CoRR(2023)
摘要
Oberman gave a stochastic control formulation of the problem of estimating
the convex envelope of a non-convex function. Based on this, we develop a
reinforcement learning scheme to approximate the convex envelope, using a
variant of Q-learning for controlled optimal stopping. It shows very promising
results on a standard library of test problems.
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