- The paper presents OR-Gym, an open-source RL library that transforms traditional OR problems into Markov Decision Processes for sequential decision-making.
- It integrates techniques like PPO and action masking, benchmarking RL against mixed-integer programming and heuristics across tasks such as knapsack and supply chain management.
- The study highlights RL’s potential in handling uncertainty and trade-offs in reward optimization versus risk management in practical applications.
Overview of OR-Gym: A Reinforcement Learning Library for Operations Research Problems
This paper presents OR-Gym, an open-source reinforcement learning (RL) library designed for addressing operations research (OR) problems. The library reframes classical optimization tasks like knapsack, multi-dimensional bin packing, multi-echelon supply chain, and multi-period asset allocation as RL environments, facilitating exploration for both OR and RL communities.
Methodological Approach
OR-Gym leverages the popular OpenAI Gym interface, integrating traditional OR problems with RL methodologies. By structuring these problems as Markov Decision Processes (MDPs), the authors enable sequential decision-making frameworks adaptable for RL models. The library includes benchmarks against mixed-integer linear programming (MILP) and heuristic methods, highlighting the adaptability of RL in these contexts. Proximal Policy Optimization (PPO) is employed as the primary RL algorithm, demonstrating its versatility across different environments.
Key Results
Knapsack Problem
In the knapsack variants, RL competes well against traditional MILP and heuristics in deterministic contexts but shows superior performance in the stochastic, online scenario. This indicates RL’s potential in handling uncertainty where conventional heuristics might struggle.
Virtual Machine Packing
For virtual machine packing, incorporating action masking within the RL setup significantly improves performance, reducing the search space and aligning closely with optimal solutions. This reinforcement underlines RL’s efficiency in environments with strict constraints.
Supply Chain Inventory Management
The multi-echelon supply chain problem highlights RL's ability to discover dynamic reordering policies that outperform static ones. However, RL still trails behind the shrinking horizon model (SHLP), where the latter leverages prior probabilistic knowledge of demand.
Asset Allocation
The multi-period asset allocation task reveals that while RL models excel in scenarios maximizing expected returns, robust optimization offers superior downside protection. The trade-off between reward potential and risk aversion is a critical decision-making aspect in financial environments.
Implications and Future Directions
The implementation of OR-Gym introduces a scalable tool for both academic research and practical applications, bridging RL and OR domains. This work sets a foundation for further cross-disciplinary investigations, particularly in integrating RL with robust and stochastic optimization techniques. Additionally, the potential for hybrid RL and mathematical programming approaches could be explored to enhance solution quality and expedite computational processes.
Conclusion
The OR-Gym library illustrates the applicability of RL in traditional OR problems, showcasing promising results, particularly under uncertainty. As AI continues to evolve, this intersection of RL and OR could lead to the development of more nuanced and efficient methodologies for solving complex industrial and operational challenges. This paper lays essential groundwork for future exploration and integration of RL frameworks in diverse OR contexts.