Dual Control Theory

By Tamer Basar. 2001

312 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Control and Systems Engineering, Reinforcement Learning, Robotics & Control

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Citations in the corpus

Optimal control theory and the linear bellman equation (2011, background): “In other words, the agent faces the problem of finding the right compromise between learning and control, a problem which is known in control theory as dual control and was originally introduced by Feldbaum (1960) (see Filatov and Unbehauen (2004) for a recent review).”
The Tools Challenge: Rapid Trial-and-Error Learning in Physical Problem Solving (2019, methodology): “This approach is often used when applying model predictive control (Feldbaum, 1960; C. Xie, Patil, Moldovan, Levine, & Abbeel, 2016; Fu, Levine, & Abbeel, 2016; Janner et al., 2018; Deisenroth & Rasmussen, 2011), also often called system identification (Chiuso & Pillonetto, 2019).”
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity (2021, background): “As noted by Duff (2002) in the RL lit-Deep erature and Feldbaum (1960); Bar-Shalom & Tse (1974) in control theory, this representation rapidly becomes intractable due to exploding state dimensionality.”
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search (2012, background): “One way to solve this dilemma [3, 10] is to augment the regular state of the agent with the information it has acquired about the dynamics.”
Deep Reinforcement Learning amidst Lifelong Non-Stationarity (2020, methodology): “As noted by Duff [12] in the RL literature and Feldbaum [14], Bar-Shalom and Tse [5] in control theory, this representation rapidly becomes intractable due to exploding state dimensionality.”