Min-max approximate dynamic programming

27 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Boosting the Actor with Dual Critic (2017, methodology): “…equation and its duality have been utilized for (approximate) planning problem (de Farias and Roy, 2004; Wang et al., 2008; Pazis and Parr, 2011; O’Donoghue et al., 2011; Malek et al., 2014; Cogill, 2015), in which the transition probability of the MDP is known and the value function or policy…”
Variational Bayesian Reinforcement Learning with Regret Bounds (2018, methodology): “, and then use that in our policy. This is a dimensionality reductionandgeneralizationstrategythat,inthenouncertaintycase,hastheoreticalguarantees[25,82],and hasbeenusedsuccessfullyincontinuouscontrol[109,110, 66,67, 60]. This could allow theuse of function approximators, such as deep neural network”
Recent advances in robust optimization: An overview (2014, methodology): “Teo (2007) presents a method for robust nonconvex optimization with arbitrary objective functions, which iteratively moves along descent directions and terminates at a robust local minimum.”