An adversarially trained autoencoder learns a convex latent space to enable rapid approximate projections that enforce nonconvex constraints in optimization and reinforcement learning.
Enming Liang, Minghua Chen, and Steven H Low
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Improving Feasibility via Fast Autoencoder-Based Projections
An adversarially trained autoencoder learns a convex latent space to enable rapid approximate projections that enforce nonconvex constraints in optimization and reinforcement learning.