ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems

Understand

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 Vehicle Routing.
• While there is a nascent literature that applies RL to these problems, there are no commonly accepted benchmarks which can be used to compare proposed approaches rigorously in terms of performance, scale, or generalizability.
• This paper aims to fill that gap.