ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems

Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games. We build on this previous work by applying RL algorithms to a selection of canonical online stochastic optimization problems with a range of practical applications: Bin Packing, Newsvendor, and V...

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Veröffentlicht in:	arXiv.org 2019-12
Hauptverfasser:	Bharathan Balaji, Bell-Masterson, Jordan, Bilgin, Enes, Damianou, Andreas, Pablo Moreno Garcia, Jain, Arpit, Luo, Runfei, Maggiar, Alvaro, Balakrishnan Narayanaswamy, Ye, Chun
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Benchmarks Domains Machine learning Optimization Resource allocation Robotics Route planning Vehicle routing
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