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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.
control-systems
motion-planning
optimal-control
control-learning
learning-control
dynamical-systems
imitation-learning
end-to-end-learning
system-identification
reinforcement-learning
trajectory-optimisation
trajectory-optimization
differentiable-programming
pontryagin-maximum-principle
inverse-reinforcement-learning
reinforcement-learning-environments
Created
2020-01-01
36 commits to master branch, last one 8 months ago