KCPO is shown to be able to train policies end-to-end with hard box constraints on controls. The use of KCPO is demonstrated in Simple Pendulum and Cartpole with continuous state and action spaces and unknown environments. KCPO brings new optimality guarantees to robot learning in unknown and nonlinear dynamical systems. This thesis introduces Koopman Constrained Policy Optimization (KCPO), combining implicitly differentiable model predictive control with a deep Koopman autoencoder. However, it retains an immense advantage over traditional deep reinforcement learning: guaranteed satisfaction of hard constraints, which is critically important for the performance and safety of robots. In contrast, classical control theory is not suitable for these unknown, nonlinear environments. Robots are now beginning to operate in unknown and highly nonlinear environments, expanding their usefulness for everyday tasks. Koopman Constrained Policy Optimization: A Koopman operator theoretic method for differentiable optimal control in roboticsÄeep reinforcement learning has recently achieved state-of-the-art results for robotic control.
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