( Preprint ) AAS 09-334 A SURVEY OF NUMERICAL METHODS FOR OPTIMAL CONTROL

By Anil V. Rao. 2009

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Citations in the corpus

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (2017, methodology): “In this work, we use a simple randomsampling shooting method [38] in which K candidate action sequences are randomly generated, the corresponding state sequences are predicted using the learned dynamics model, the rewards for all sequences are calculated, and the candidate action sequence with the h”
Benchmarking Model-Based Reinforcement Learning (2019, background): “Random Shooting (RS) [40, 39]: RS optimizes the action sequence at:t+τ to maximize the expected planning reward under the learned dynamics model, i.”
An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation (2017, methodology): “A more general framework is described in [51] and briefly discussed in Section §9.”
Thinking fast and slow: Optimization decomposition across timescales (2017, background): “However, most existing numerical methods for optimal control (see [38] for a survey) do not scale well, and decomposing large scale problems into smaller subproblems is often required.”
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning (2021, methodology): “We use random shooting (Rao, 2009) to randomly generate K candidate option sequences, predict the corresponding Z sequences using the learned model ˆ f , compute the rewards for all sequences, and pick the candidate action sequence leading to a latent state closest to the goal, according to…”
Self-supervised Learning of Image Embedding for Continuous Control (2019, methodology): “One approach would be Model Predictive Control (MPC) methods [Rao, 2009, Nagabandi et al., 2017], where a full sequence of T actions is selected based on the solution to Despite considerable progress in the area [Finn et al., 2016, Lee et al., 2018], it is still challenging to learn a visual model…”
An Optimal Control Approach to Sequential Machine Teaching (2018, background): “For contemporary surveys of direct and indirect numerical approaches, see Rao (2009); Betts (2010).”