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Found 5 papers, 1 papers with code
Policies for the Dynamic Traveling Maintainer Problem with Alerts
On the other hand, because the available resources to serve a network of geographically dispersed assets are typically limited.
Optimizing Biomanufacturing Harvesting Decisions under Limited Historical Data
We adopt a Bayesian approach to update the unknown parameters of the growth-rate distributions, and use the resulting posterior distributions to characterize the impact of model risk on fermentation output variability.
Algorithms for slate bandits with non-separable reward functions
In this paper, we study a slate bandit problem where the function that determines the slate-level reward is non-separable: the optimal value of the function cannot be determined by learning the optimal action for each slot.
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution.
Remaining Useful Lifetime Prediction via Deep Domain Adaptation
We propose a Domain Adversarial Neural Network (DANN) approach to learn domain-invariant features that can be used to predict the RUL in the target domain.
