Relaxing dynamic programming

By Bo Lincoln, Anders Rantzer. IEEE Transactions on Automatic Control, 2006

333 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/d3bf8645f48e571840c8e200c97a117f04fd50c4

How researchers see this paper

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

In Artificial Intelligence

1. Hanquan Lin
2. B. Recht
3. Qinglai Wei
4. Derong Liu
5. Horia Mania
6. Stephen Tu
7. Stephen P. Boyd
8. Brendan O'Donoghue

In Control and Systems Engineering

1. Brendan O'Donoghue
2. B. Recht
3. Stephen P. Boyd
4. Yang Wang
5. Horia Mania
6. Nikolai Matni
7. Stephen Tu
8. Alberto Bemporad

In Industrial and Manufacturing Engineering

1. Qinglai Wei
2. Lars Grüne
3. Stephen P. Boyd
4. Derong Liu
5. Hanquan Lin
6. B. Recht

Citations in the corpus

• Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator (2018, background): “The first part follows from Proposition 1 of Lincoln and Rantzer [17] on value iteration.”
• Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems (2016, methodology): “in [47], [52], [53], [55], [59], and [60].”
• Approximation Properties of Receding Horizon Optimal Control (2016, background): “1 was first formulated in this way in [30], motivated by ideas from [36].”
• Approximate dynamic programming via iterated Bellman inequalities (2015, result): “Similar ideas and methods can also be found in papers by Savorgnan, Lasserre and Diehl [SLD09], Bertsimas and Caramanis [BC06], and Lincoln and Rantzer [LR06, Ran06].”
• Adaptive Critic Nonlinear Robust Control: A Survey (2017, neutral): “They are “adaptive critic designs” [29]–[31], “ADP” [32], [33], “approximate dynamic programming” [18], [27], [34], “neural dynamic programming” [35], [36], “neuro-dynamic programming” [37], “reinforcement learning” [13], [34] including Q-learing [38], and “relaxed dynamic programming” [39], [40].”
• A survey on explicit model predictive control (2009, background): “Based on a dynamic programming formulation of the finite-horizon optimal control problem, in [49] the authors propose an approach to relax optimality within a prescribed bound in favor of the reduced complexity of the solution, which in the case of linear systems and piecewise affine convex costs le”
• Adaptive Dynamic Programming for Control: A Survey and Recent Advances (2021, background): “The convergence proof of VI can be found in [76], [84], and [85].”