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It is shown that priors sensitive to distribution shift can lose half of their mechanistic information under a small distribution shift, motivating physically grounded priors for safety-critical applications.
Across scenarios, including game-playing and robotics, spanning both discrete and continuous domains, agents trained with the proposed prioritization method achieve 15-57% higher average success rates and improve sample efficiency by 35-55% compared to previous approaches.
The COMPASS algorithm for OCTSP is introduced, which combines search with learning-accelerated routing by orchestrating parallel sub-solvers and can reach exact solutions in time exponential in cluster size rather than instance size.
This work introduces an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions, which are then further optimized by a genetic algorithm, enabling real-time and interactive routing at scale.
Dimension reduction for dynamical systems is standard practice, and the standard route is spectral: model the transfer (Koopman) operator by its leading modes. We show that on systems assembled from several weakly interacting components --- a structure common in physical and biological settings --- this may either requ...
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