标题： 基于生成式AI的多智能体强化学习走向分布式智能代理：生成式RL代理视角 显示英文标题

标题： GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective

摘要： 多智能体强化学习面临根本性挑战，传统方法未能克服这些问题：联合动作空间呈指数增长，非平稳环境导致同时学习产生移动目标，以及部分可观测性限制了协调能力。 当前方法仍然处于被动反应状态，采用刺激-反应机制，在面对新场景时会失效。 我们主张通过基于生成式人工智能的强化学习，实现从被动反应到主动智能的范式转变。 本文主张重新定义智能体，不应将其视为孤立的策略优化器，而应视为能够合成复杂多智能体动态并基于对未来交互的预测理解做出前瞻性决策的复杂生成模型。 生成式强化学习智能体无需对即时观察做出反应，而是可以建模环境演变，预测其他智能体的行为，生成协调的动作序列，并考虑长期动态进行战略推理。 这种方法利用生成式人工智能的模式识别和生成能力，实现主动决策、通过增强通信实现无缝协调，并动态适应不断变化的场景。 我们设想这种范式转变将为分布式智能开辟前所未有的可能性，超越个体优化，走向体现真正协作智能的涌现集体行为。 其影响范围涵盖自主系统、机器人技术和人机协作，有望解决在传统被动框架下难以处理的协调挑战。 显示英文摘要

摘要： Multi-agent reinforcement learning faces fundamental challenges that conventional approaches have failed to overcome: exponentially growing joint action spaces, non-stationary environments where simultaneous learning creates moving targets, and partial observability that constrains coordination. Current methods remain reactive, employing stimulus-response mechanisms that fail when facing novel scenarios. We argue for a transformative paradigm shift from reactive to proactive multi-agent intelligence through generative AI-based reinforcement learning. This position advocates reconceptualizing agents not as isolated policy optimizers, but as sophisticated generative models capable of synthesizing complex multi-agent dynamics and making anticipatory decisions based on predictive understanding of future interactions. Rather than responding to immediate observations, generative-RL agents can model environment evolution, predict other agents' behaviors, generate coordinated action sequences, and engage in strategic reasoning accounting for long-term dynamics. This approach leverages pattern recognition and generation capabilities of generative AI to enable proactive decision-making, seamless coordination through enhanced communication, and dynamic adaptation to evolving scenarios. We envision this paradigm shift will unlock unprecedented possibilities for distributed intelligence, moving beyond individual optimization toward emergent collective behaviors representing genuine collaborative intelligence. The implications extend across autonomous systems, robotics, and human-AI collaboration, promising solutions to coordination challenges intractable under traditional reactive frameworks.