Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics
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Abstract (arXiv)
We study regenerative stopping problems in which the system starts anew whenever the controller decides to stop and the long-term average cost is to be minimized. Traditional model-based solutions involve estimating the underlying process from data and computing strategies for the estimated model. In this paper, we compare such solutions to deep reinforcement learning and imitation learning which involve learning a neural network policy from simulations. We evaluate the different approaches on a real-world problem of shipping consolidation in logistics and demonstrate that deep learning can be effectively used to solve such problems.
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References · 4
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
- 2017Learning Combinatorial Optimization Algorithms over Graphscited by 561
- 2020Reinforcement Learning for Combinatorial Optimization: A Surveycited by 236
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