Neural operators approximate the solution operator for multi-task optimal control, generalizing to new tasks and enabling efficient adaptation via branch-trunk structure and meta-training.
Sqil: Imitation learning via reinforcement learning with sparse rewards.arXiv preprint arXiv:1905.11108
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Neural Operators for Multi-Task Control and Adaptation
Neural operators approximate the solution operator for multi-task optimal control, generalizing to new tasks and enabling efficient adaptation via branch-trunk structure and meta-training.