Stochastic learning and optimization - A sensitivity-based approach

By Xi-Ren Cao. Annual Reviews in Control, 2007

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Pathologies of temporal difference methods in approximate dynamic programming (2010, methodology): “…one of the major methodologies for approximate DP (see the books by Bertsekas and Tsitsiklis [BeT96], Sutton and Barto [SuB98], Gosavi [Gos03], Cao [Cao07], Chang, Fu, Hu, and Marcus [CFH07], Meyn [Mey07], Powell [Pow07], and Borkar [Bor08]; the textbook [Ber07] together with its on-line chapter…”
Algorithms for Reinforcement Learning (2010, methodology): “Policy gradient In this section, we review policy gradient methods (for a sensitivitybased approach, see Cao, 2007).”
Q-learning and enhanced policy iteration in discounted dynamic programming (2010, methodology): “…one of the major methodologies for approximate DP (see the books by Bertsekas and Tsitsiklis [BeT96], Sutton and Barto [SuB98], Gosavi [Gos03], Cao [Cao07], Chang, Fu, Hu, and Marcus [CFH07], Meyn [Mey07], Powell [Pow07], and Borkar [Bor08]; the textbook [Ber07] together with its on-line chapter…”
A unified view of entropy-regularized Markov decision processes (2017, neutral): “1, Kakade and Langford, 2002, Lemma 6.1, Cao, 2007).”
Event-Based Sensor Data Scheduling: Trade-Off Between Communication Rate and Estimation Quality (2013, background): “These events may contain useful information about the system [3], and using an appropriate event-based scheduler, the performance of the estimator can be improved.”