Constrained policy optimization for stochastic optimal control under nonstationary uncertainties via Markov embeddability and finite approximation.
NLPModels.jl: Data structures for optimization models
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A condensed-space interior-point method factorized on GPU/SIMD hardware solves constrained LQ-MPC problems an order of magnitude faster than CPU when the number of inputs is small and the horizon moderate.
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Constrained Policy Optimization for Stochastic Optimal Control under Nonstationary Uncertainties
Constrained policy optimization for stochastic optimal control under nonstationary uncertainties via Markov embeddability and finite approximation.
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Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems
A condensed-space interior-point method factorized on GPU/SIMD hardware solves constrained LQ-MPC problems an order of magnitude faster than CPU when the number of inputs is small and the horizon moderate.