How can I scale MARL for large web apps?
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning
SrSv improves multi-agent reinforcement learning (MARL) efficiency and scalability, especially for large numbers of agents, by combining sequential decision-making with individual value estimations. Agents act in sequence, considering previous actions, and learn individually, leading to better coordination and faster training.
Key to LLM-based multi-agent systems is the autoregressive action rollout, similar to how LLMs generate text, and the attention mechanism for value estimation, enabling efficient credit assignment in complex multi-agent scenarios. This combination allows for capturing complex agent interdependencies and handling varying agent populations, which are important considerations for large-scale LLM-based multi-agent application development.