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We present a framework for safety-critical optimal control of physical systems based on denoising diffusion probabilistic models (DDPMs).
Model-Based Reinforcement Learning for Atari
Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., Mohiuddin, A., Sepassi, R., Tucker, G., and Michalewski, H · 1903
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Misra, D · 1908
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Reinforcement Learning: A Survey
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Gaussian Processes in Machine Learning
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Adam: A method for stochastic optimization
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Control Barrier Function Based Quadratic Programs for Safety Critical Systems
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
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Deep Reinforcement Learning: An Overview
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Soft Actor-Critic Algorithms and Applications
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Reinforcement Learning: An introduction, 2018
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Control barrier functions: Theory and applications
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End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
Cheng, R., Orosz, G., Murray, R. M., and Burdick, J. W · 2019
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Group Normalization
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Diffusion Models Beat GANs on Image Synthesis
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Improved Denoising Diffusion Probabilistic Models
Nichol, A. and Dhariwal, P · 2021
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Safety-Critical Kinematic Control of Robotic Systems
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Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
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Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
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Song, Y. and Ermon, S · 2019
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Denoising diffusion probabilistic models
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Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control
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Planning with Diffusion for Flexible Behavior Synthesis
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Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
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