![]() ![]() 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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