ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems

doi:doi:10.57702/64p212sb

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.

BibTex:

@dataset{Bharathan_Balaji_and_Jordan_Bell-Masterson_and_Enes_Bilgin_and_Andreas_Damianou_and_Pablo_Moreno_Garcia_and_Arpit_Jain_and_Runfei_Luo_and_Alvaro_Maggiar_and_Balakrishnan_Narayanaswamy_and_Chun_Ye_2024,
    abstract = {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.},
    author = {Bharathan Balaji and Jordan Bell-Masterson and Enes Bilgin and Andreas Damianou and Pablo Moreno Garcia and Arpit Jain and Runfei Luo and Alvaro Maggiar and Balakrishnan Narayanaswamy and Chun Ye},
    doi = {10.57702/64p212sb},
    institution = {No Organization},
    keyword = {'Bin Packing', 'Newsven-dor', 'Online Stochastic Optimization', 'Reinforcement Learning', 'Vehicle Routing'},
    month = {dec},
    publisher = {TIB},
    title = {ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems},
    url = {https://service.tib.eu/ldmservice/dataset/orl--reinforcement-learning-benchmarks-for-online-stochastic-optimization-problems},
    year = {2024}
}