Model Predictive Control Using Neural Networks [25 Years Ago]

By A. Draeger, S. Engell, H. Ranke. 1995

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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018, methodology): “Parametric function approximators have also been used extensively in MBRL [21], [22], [23], [24], but”
MOPO: Model-based Offline Policy Optimization (2020, methodology): “Our approach builds upon the wealth of prior work on model-based online RL methods that models the dynamics by Gaussian processes [11], local linear models [41, 37], neural network function approximators [14, 20, 13], and neural video prediction models [15, 31].”
When to Trust Your Model: Model-Based Policy Optimization (2019, methodology): “Neural network predictive models (Draeger et al., 1995; Gal et al., 2016; Depeweg et al., 2016; Nagabandi et al., 2018), are appealing because they allow for algorithms that combine the sample efﬁciency of a model-based approach with the asymptotic performance of high-capacity function…”
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit (2017, background): “, ARX, ARMA, NARX, and NARMAX [3] models), and neural network models [27, 14, 54, 1], to name only a few.”
How to train your robot with deep reinforcement learning: lessons we have learned (2021, background): “Model-based algorithms such as Draeger et al. (1995) choose the optimal action by leveraging a model of the environment.”