研究人员开发了新的多智能体强化学习（MARL）框架，以增强复杂场景下的协作策略。一种方法MA-AC-MPC将基于模型的控制与MARL相结合，实现安全且动态可行的动作，并在追逐-躲避和异构无人机-漫游车着陆任务中取得了成功。另一个框架ND-MARL专注于网络分布式MARL，用于四旋翼无人机共识控制，在无需重新训练的情况下，展示了高达250个智能体的令人印象深刻的零样本可扩展性。 AI
影响 这些MARL的进步有望为机器人和群体系统带来更强大、更具可扩展性的协作AI。
排序理由 该集群包含两篇详细介绍多智能体强化学习新算法和框架的研究论文。
在 arXiv cs.MA (Multiagent) 阅读 →
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arXiv:2606.06011v1 Announce Type: cross Abstract: In this work, we propose a framework that combines multi-agent reinforcement learning (MARL) with model-based control to achieve safe, dynamically feasible actions in cooperative multi-agent tasks. Multi-agent reinforcement learni…
In this work, we propose a framework that combines multi-agent reinforcement learning (MARL) with model-based control to achieve safe, dynamically feasible actions in cooperative multi-agent tasks. Multi-agent reinforcement learning provides the advantage of learning cooperative …
arXiv:2606.02107v1 Announce Type: cross Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control. Compared to conventional multi-agent MARL formulations that rely on centralized planning or fully d…
This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control. Compared to conventional multi-agent MARL formulations that rely on centralized planning or fully decentralized execution, ND-MARL incorporates the s…