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, Newsven-dor, and Vehicle Routing.

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Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin, Andreas Damianou, Pablo Moreno Garcia, Arpit Jain, Runfei Luo, Alvaro Maggiar, Balakrishnan Narayanaswamy, Chun Ye (2024). Dataset: ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems. https://doi.org/10.57702/64p212sb

DOI retrieved: December 3, 2024

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Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.1911.10641
Author Bharathan Balaji
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Jordan Bell-Masterson
Enes Bilgin
Andreas Damianou
Pablo Moreno Garcia
Arpit Jain
Runfei Luo
Alvaro Maggiar
Balakrishnan Narayanaswamy
Chun Ye
Homepage https://github.com/awslabs/or-rl-benchmarks