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Achieving up to 70.6% fewer samples for optimal closed-loop performance could revolutionize how we approach online adaptation in reinforcement learning.
Forget painstakingly tuning MPC controllers by hand: this method learns optimal humanoid locomotion policies by aligning MPC cost functions with high-fidelity RL data.
