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Found 3 papers, 1 papers with code
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks
In neural architecture search (NAS) methods based on latent space optimization (LSO), a deep generative model is trained to embed discrete neural architectures into a continuous latent space.
Q-learning for Optimal Control of Continuous-time Systems
By introducing the Q-function for continuous-time systems, policy iteration based QL (PIQL) and value iteration based QL (VIQL) algorithms are proposed for learning the optimal control policy from real system data rather than using mathematical system model.
Data-based approximate policy iteration for nonlinear continuous-time optimal control design
Firstly, a model-free policy iteration algorithm is derived for constrained optimal control problem and its convergence is proved, which can learn the solution of HJB equation and optimal control policy without requiring any knowledge of system mathematical model.
