Deep Generative Models for Decision-Making and Control
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Abstract (arXiv)
Deep model-based reinforcement learning methods offer a conceptually simple approach to the decision-making and control problem: use learning for the purpose of estimating an approximate dynamics model, and offload the rest of the work to classical trajectory optimization. However, this combination has a number of empirical shortcomings, limiting the usefulness of model-based methods in practice. The dual purpose of this thesis is to study the reasons for these shortcomings and to propose solutions for the uncovered problems. Along the way, we highlight how inference techniques from the contemporary generative modeling toolbox, including beam search, classifier-guided sampling, and image inpainting, can be reinterpreted as viable planning strategies for reinforcement learning problems.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 106 of its 165 references that are in the map. First posted on arXiv 15 Jun 2023.
Builds on
106 of 165 references.
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