Simulation optimization: a review, new developments, and applications

By Michael C. Fu, Fred W. Glover, Jay April. 2005

448 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Mathematical Optimization, Economics & Operations Research

How researchers see this paper

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

In Computer Networks and Communications

1. Sergey Levine
2. Pieter Abbeel
3. Richard M. Murray
4. Joel W. Burdick
5. Gábor Orosz
6. Michael I. Jordan
7. Philipp Moritz
8. John Schulman

In Management Science and Operations Research

1. Sergey Levine
2. Pieter Abbeel
3. John Schulman
4. Joel W. Burdick
5. Richard M. Murray
6. Michael I. Jordan
7. Nikolaos V. Sahinidis
8. Philipp Moritz

In Mathematical Optimization

1. Joel W. Burdick
2. Pieter Abbeel
3. Michael I. Jordan
4. Nikolaos V. Sahinidis
5. Sergey Levine
6. Satyajith Amaran
7. Scott J. Bury
8. Bikram Sharda

In Economics & Operations Research

1. Pieter Abbeel
2. Michael I. Jordan
3. Sergey Levine
4. John Schulman

Citations in the corpus

• Trust Region Policy Optimization (2015, methodology): “Most policy optimization methods can be classified into three broad categories: policy iteration methods, which alternate between estimating the value function under the current policy and updating the policy to choose better actions [1]; policy gradient methods, which use an estimator of the gradie”
• A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “The policy search can be carried out in two paradigms: gradient-free (Fu et al., 2005; Szita and L¨orincz, 2006) and gradient-based.”
• End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks (2019, methodology): “…to maximize long-term reward J ( π ) using (a) policy iteration meth-ods (Bertsekas 2005), (b) derivative-free optimization meth-ods that optimize the return as a function of policy parameters (Fu, Glover, and April 2005), or (c) policy gradient methods (Peters and Schaal 2008; Silver et al. 2014).”
• Simulation optimization: a review of algorithms and applications (2014, background): “Fu et al. [62] provide a concise, updated version of all of this, and also talk about estimation of distribution algorithms.”
• Learning surrogate models for simulation‐based optimization (2014, unknown): “2 2 2 [2, 2] 2 2 [6, 8] [6, 11] [12, 15] [12, 15] 2 3 3 3 3 3 [5, 12] [5, 10] [12, 14] [12, 14] 2 4 [3, 4] [3, 4] [3, 4] [3, 4] [8, 11] [8, 10] [11, 12] [11, 12] 2 5 [2, 4] [2, 4] [2, 5] [2, 5] [3, 12] [4, 11] [10, 16] [10, 16] 2 6 [5, 6] [6, 6] [5, 6] [6, 6] [7, 10] [6, 7] [11, 13] [11, 13] 2 7 [4,”