SRMT: Shared Memory for Multi-agent Lifelong Pathfinding

Multi-agent reinforcement learning (MARL) demonstrates significant progress in solving cooperative and competitive multi-agent problems in various environments. One of the principal challenges in MARL is the need for explicit prediction of the agents' behavior to achieve cooperation. To resolve this issue, we propose the Shared Recurrent Memory Transformer (SRMT) which extends memory transformers to multi-agent settings by pooling and globally broadcasting individual working memories, enabling agents to exchange information implicitly and coordinate their actions. We evaluate SRMT on the Partially Observable Multi-Agent Pathfinding problem in a toy Bottleneck navigation task that requires agents to pass through a narrow corridor and on a POGEMA benchmark set of tasks. In the Bottleneck task, SRMT consistently outperforms a variety of reinforcement learning baselines, especially under sparse rewards, and generalizes effectively to longer corridors than those seen during training. On POGEMA maps, including Mazes, Random, and MovingAI, SRMT is competitive with recent MARL, hybrid, and planning-based algorithms. These results suggest that incorporating shared recurrent memory into the transformerbased architectures can enhance coordination in decentralized multi-agent systems. The source code for training and evaluation is available on GitHub: https://github.com/Aloriosa/srmt. Multi-agent systems have significant potential to solve complex problems through distributed intelligence and collaboration. However, coordinating the interactions between multiple agents remains challenging, often requiring sophisticated communication protocols and decision-making mechanisms. We propose a novel approach to address this challenge by introducing a shared memory as a global workspace for agents to coordinate behavior. The global workspace theory (Baars, 1988) suggests that in the brain, there are independent functional modules that can cooperate by broadcasting information through a global workspace.