Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control

2025-10-23

AI Summary

In autonomous control of multi-stage industrial processes, balancing local specialization with global coordination remains challenging. Method: We propose the first multi-agent reinforcement learning (MARL) benchmark environment tailored to sequential industrial recycling tasks (sorting + baling), bridging the gap between academic benchmarks and real-world industrial requirements. Built upon SortingEnv and ContainerGym, our scalable simulation platform enables systematic comparison of modular multi-agent versus monolithic agent architectures, augmented with an action masking mechanism to explicitly constrain the feasible action space according to industrial constraints. Results: Action masking substantially improves training stability and final performance for both architectures, significantly narrowing their performance gap. Under realistic action constraints, the advantage of specialization diminishes markedly. This work establishes a new paradigm for evaluating transferability in industrial RL and highlights the critical role of action-space modeling in control policy design.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial control problems, limiting their transferability to real-world applications. This study introduces an enhanced industry-inspired benchmark environment that combines tasks from two existing benchmarks, SortingEnv and ContainerGym, into a sequential recycling scenario with sorting and pressing operations. We evaluate two control strategies: a modular architecture with specialized agents and a monolithic agent governing the full system, while also analyzing the impact of action masking. Our experiments show that without action masking, agents struggle to learn effective policies, with the modular architecture performing better. When action masking is applied, both architectures improve substantially, and the performance gap narrows considerably. These results highlight the decisive role of action space constraints and suggest that the advantages of specialization diminish as action complexity is reduced. The proposed benchmark thus provides a valuable testbed for exploring practical and robust multi-agent RL solutions in industrial automation, while contributing to the ongoing debate on centralization versus specialization.

Problem

• Balancing local specialization and global coordination in industrial control
• Addressing reward design and action space challenges in RL adoption
• Providing industry-relevant benchmark for multi-agent reinforcement learning evaluation

Innovation

• Multi-agent reinforcement learning benchmark for industrial control
• Modular specialized agents versus monolithic centralized control
• Action masking improves policy learning and reduces complexity