SRMT: Shared Memory for Multi-agent Lifelong Pathfinding
This addresses coordination problems in decentralized multi-agent systems, but it is incremental as it builds on existing transformer and memory architectures.
The paper tackles the challenge of explicit behavior prediction for cooperation in multi-agent reinforcement learning by proposing the Shared Recurrent Memory Transformer (SRMT), which pools and broadcasts individual memories to enable implicit coordination. In a Bottleneck navigation task, SRMT outperforms baselines, especially under sparse rewards, and generalizes to longer corridors, while being competitive on POGEMA benchmarks.
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 transformer-based 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.