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Multi-Agent Reinforcement Learning (MARL) addresses complex decision-making in environments with multiple autonomous agents through decentralized learning and coordinated strategies. This paper examines MARL’s ability to balance individual objectives with collective goals in scenarios ranging from cooperative to competitive interactions. Key challenges include environmental non-stationarity, partial observability, and scalable credit assignment mechanisms. Recent advances leverage centralized training with decentralized execution (CTDE) frameworks, as demonstrated by [1], who introduced the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Their work highlights improved stability and performance in mixed cooperative-competitive tasks through centralized value function estimation. Experimental results in autonomous vehicle coordination and drone swarm navigation demonstrate MARL’s potential for real-world applications. The study concludes with an analysis of open research directions, including sample efficiency improvements and theoretical guarantees for convergence. This work contributes to developing robust MARL systems capable of adaptive decision-making in dynamic multi-agent ecosystems.

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Multi-Agent Reinforcement Learning for Complex Decision-Making

  • 会议论文

  • 发表日期：

    2026年

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  • Nandita Giri,
  • Madhav Agarwal,
  • Lakshmojee Koduru,
  • Shubneet,
  • Ram Mohan Polam,
  • Anushka Raj Yadav,
  • Navjot Singh Talwandi

摘要

Multi-Agent Reinforcement Learning (MARL) addresses complex decision-making in environments with multiple autonomous agents through decentralized learning and coordinated strategies. This paper examines MARL’s ability to balance individual objectives with collective goals in scenarios ranging from cooperative to competitive interactions. Key challenges include environmental non-stationarity, partial observability, and scalable credit assignment mechanisms. Recent advances leverage centralized training with decentralized execution (CTDE) frameworks, as demonstrated by [1], who introduced the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Their work highlights improved stability and performance in mixed cooperative-competitive tasks through centralized value function estimation. Experimental results in autonomous vehicle coordination and drone swarm navigation demonstrate MARL’s potential for real-world applications. The study concludes with an analysis of open research directions, including sample efficiency improvements and theoretical guarantees for convergence. This work contributes to developing robust MARL systems capable of adaptive decision-making in dynamic multi-agent ecosystems.