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Found 3 papers, 1 papers with code
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.
Machine Learning based Simulation Optimisation for Trailer Management
In many situations, simulation models are developed to handle complex real-world business optimisation problems.
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