IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL (2306.11551v2)

Abstract: We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning (MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a platform for benchmarking the scalability of cooperative MARL methods in real-world engineering applications. In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition. Specifically, each agent plans inspections and repairs for a specific system component, aiming to minimise maintenance costs while cooperating to minimise system failure risk. With IMP-MARL, we release several environments including one related to offshore wind structural systems, in an effort to meet today's needs to improve management strategies to support sustainable and reliable energy systems. Supported by IMP practical engineering environments featuring up to 100 agents, we conduct a benchmark campaign, where the scalability and performance of state-of-the-art cooperative MARL methods are compared against expert-based heuristic policies. The results reveal that centralised training with decentralised execution methods scale better with the number of agents than fully centralised or decentralised RL approaches, while also outperforming expert-based heuristic policies in most IMP environments. Based on our findings, we additionally outline remaining cooperation and scalability challenges that future MARL methods should still address. Through IMP-MARL, we encourage the implementation of new environments and the further development of MARL methods.

• The paper introduces IMP-MARL, a novel suite designed to benchmark cooperative MARL methods in large-scale infrastructure management planning.
• It employs a Dec-POMDP framework to simulate realistic engineering environments and tests various MARL algorithms including CTDE-based approaches.
• The study shows that MARL methods outperform heuristic policies while addressing scalability and cooperation challenges in sustainable energy systems.

An Overview of the IMP-MARL Suite for Large-Scale Infrastructure Management Planning Using Multi-Agent Reinforcement Learning

The paper introduces IMP-MARL, an open-source suite designed for multi-agent reinforcement learning (MARL) environments specifically for large-scale Infrastructure Management Planning (IMP). It presents a robust platform aimed at benchmarking the scalability of cooperative MARL methods through realistic engineering applications. The core emphasis is on improving IMP strategies to bolster sustainable and reliable energy systems, addressing a pressing need driven by societal and environmental demands.

The Conceptual Framework

The IMP framework is centered around managing a multi-component engineering system vulnerable to component failure. Each agent's role is to devise inspection and repair strategies for an individual system component. These strategies aim to minimize maintenance costs while cooperating with other agents to reduce system failure risks.

Environment Composition

IMP-MARL features a variety of environments, including one dedicated to offshore wind structural systems. Within these environments, agents operate based on damage probabilities of components, planning for maintenance actions, and theoretically modeled through a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Each agent operates with limited local observations while the overall system state is modeled globally.

Benchmarking and Performance Evaluation

A benchmarking campaign is conducted within the paper to test a suite of MARL methods, including:

1. Centralized Training with Decentralized Execution (CTDE): Evidently, CTDE approaches demonstrate superior scalability with increased agent numbers, compared to centralized or purely decentralized methods.
2. Tested Algorithms: The suite includes QMIX, QVMix, QPLEX, COMA, FACMAC, alongside the decentralized IQL and the centralized DQN.
3. Comparison with Heuristic Policies: The MARL methods are benchmarked against expert-based heuristic policies, with results indicating that MARL methods generally outperform these traditional methods, although the relative advantage diminishes when the number of agents becomes very large.

Implications and Future Directions

The results underscore the feasibility of applying cooperative MARL methods to real-world engineering challenges in IMP, demonstrating that these methods can generate more effective policies than traditional heuristic approaches. Nevertheless, the study identifies persistent challenges, such as ensuring robust cooperation among a large number of agents and improving stability in environments characterized by global cost triggers due to local actions.

The paper encourages the development of additional MARL environments, enabling advancements in methods that can handle more sophisticated and realistic scenarios. Future research could explore new modeling paradigms, such as mean-field games, to deal with environments having a substantial number of components.

Conclusion

IMP-MARL serves as a comprehensive tool for advancing research in cooperative MARL within the domain of infrastructure management planning. The suite provides a transparent and reproducible platform, which researchers can further expand upon to ensure robust and scalable solutions for engineering systems management, reflecting the paper's commitment to facilitating continuous progress in this critical societal arena.