ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems

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, 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. For each problem we apply both standard approaches as well as newer RL algorithms and analyze results. In each case, the performance of the trained RL policy is competitive with or superior to the corresponding baselines, while not requiring much in the way of domain knowledge. This highlights the potential of RL in real-world dynamic resource allocation problems.

References in corpus (5)

• Learning Combinatorial Optimization Algorithms over Graphs
• Benchmarking Deep Reinforcement Learning for Continuous Control
• Distributed Prioritized Experience Replay
• Learning Dexterous In-Hand Manipulation
• A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems

Cited by in corpus (7)

• Reinforcement Learning for Ridesharing: An Extended Survey
• Deep Reinforcement Learning and Transportation Research: A Comprehensive Review
• Neural Stochastic Dual Dynamic Programming
• Average Reward Adjusted Discounted Reinforcement Learning: Near-Blackwell-Optimal Policies for Real-World Applications
• Towards Automatic Actor-Critic Solutions to Continuous Control
• Reinforcement Learning for Assignment Problem with Time Constraints
• Where the Action is: Let's make Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problems work!