Two Optimal Value Functions in Parametric Conic Linear Programming
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Optimization Theory and Applications"
DOI: 10.1007/s10957-021-01959-z
Abstract: We consider the conic linear program given by a closed convex cone in an Euclidean space and a matrix, where vector on the right-hand-side of the constraint system and the vector defining the objective function… read more here.
Keywords: optimal value; value functions; value; two optimal ... See more keywords
Automatic Pan–Tilt Camera Control for Learning Dirichlet Process Gaussian Process (DPGP) Mixture Models of Multiple Moving Targets
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Automatic Control"
DOI: 10.1109/tac.2018.2849584
Abstract: Information value functions based on the Kullback–Leibler (KL) divergence have been shown the most effective for planning sensor measurements by means of greedy strategies. The problem of optimizing information value over a finite time horizon… read more here.
Keywords: information value; process; value functions; control ... See more keywords
Robust Losses for Learning Value Functions
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3213503
Abstract: Most value function learning algorithms in reinforcement learning are based on the mean squared (projected) Bellman error. However, squared errors are known to be sensitive to outliers, both skewing the solution of the objective and… read more here.
Keywords: learning value; losses learning; bellman error; robust losses ... See more keywords