SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning

2025-03-03

AI Summary

Addressing the dual challenges of credit assignment and variable team size in large-scale multi-agent reinforcement learning (MARL), this paper proposes SrSv—a synergistic framework integrating sequential rollouts with sequential value estimation. SrSv innovatively employs an autoregressive Transformer to jointly model both action generation and value estimation as sequential processes, explicitly capturing the dynamic interdependence between policy distributions and value functions. Leveraging attention-driven sequential decision-making and end-to-end joint optimization, SrSv achieves efficient and scalable cooperative control. Empirical evaluation on standard benchmarks—including Multi-Agent MuJoCo, SMAC, and DubinsCars—demonstrates substantial improvements in training efficiency. Notably, on the 1024-agent DubinsCars task, SrSv surpasses prior methods, validating its strong scalability and generalization capability across heterogeneous agent populations and complex dynamics.

Technology Category

• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Multi-Robot Systems

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Although multi-agent reinforcement learning (MARL) has shown its success across diverse domains, extending its application to large-scale real-world systems still faces significant challenges. Primarily, the high complexity of real-world environments exacerbates the credit assignment problem, substantially reducing training efficiency. Moreover, the variability of agent populations in large-scale scenarios necessitates scalable decision-making mechanisms. To address these challenges, we propose a novel framework: Sequential rollout with Sequential value estimation (SrSv). This framework aims to capture agent interdependence and provide a scalable solution for cooperative MARL. Specifically, SrSv leverages the autoregressive property of the Transformer model to handle varying populations through sequential action rollout. Furthermore, to capture the interdependence of policy distributions and value functions among multiple agents, we introduce an innovative sequential value estimation methodology and integrates the value approximation into an attention-based sequential model. We evaluate SrSv on three benchmarks: Multi-Agent MuJoCo, StarCraft Multi-Agent Challenge, and DubinsCars. Experimental results demonstrate that SrSv significantly outperforms baseline methods in terms of training efficiency without compromising convergence performance. Moreover, when implemented in a large-scale DubinsCar system with 1,024 agents, our framework surpasses existing benchmarks, highlighting the excellent scalability of SrSv.

Problem

• Addresses credit assignment in complex MARL environments
• Provides scalable decision-making for variable agent populations
• Enhances training efficiency without compromising convergence performance

Innovation

• Sequential action rollout using Transformer model
• Sequential value estimation for policy interdependence
• Attention-based model integrating value approximation