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system-identification

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wanxinjin / Pontryagin-Differentiable-Programming

Star 100

A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.

reinforcement-learning trajectory-optimisation motion-planning dynamical-systems control-systems trajectory-optimization optimal-control system-identification imitation-learning inverse-reinforcement-learning end-to-end-learning control-learning differentiable-programming reinforcement-learning-environments learning-control pontryagin-maximum-principle
  • Updated Jun 30, 2022
  • Python

lawmurray / Birch

Star 88

A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

machine-learning statistics monte-carlo automatic-differentiation probabilistic-programming bayesian-inference system-identification bayesian-data-analysis sequential-monte-carlo probabilistic-programming-language birch automatic-marginalization automatic-conjugacy automatic-conditioning
  • Updated Oct 29, 2022
  • C++
LowLevelParticleFilters.jl

baggepinnen / LowLevelParticleFilters.jl

Star 74

State estimation, smoothing and parameter estimation using Kalman and particle filters.

estimation particle-filter dynamical-systems control-systems parameter-estimation bayesian-inference unscented-kalman-filter data-assimilation state-estimation system-identification kalman-filter monte-carlo-methods sequential-monte-carlo log-likelihood extended-kalman-filter state-estimation-filters state-estimation-algorithms prediction-error-method
  • Updated Aug 29, 2022
  • Julia

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