Related papers for multi-agent reforcement learning.
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Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies
设计了two-player中心化采样缓冲池（该buffer可以利用彼此的动作进行训练），设计了信号中介策略框架（每个agent的策略在每个episode开始前condition on一个信号，该信号表征多智能体策略之间的均衡）
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Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
受到相关均衡CE的启发，设计一个中心信号控制器，每个agent的策略都依托于这个隐含全局信息的信号中；主要学习目标有三个：对齐智能体策略和协作信号、减轻其他智能体策略的不确定性以降低协作难度、学到多样性策略；
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Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning
受限MARL一方面要最小化累积cost，另一方面要满足约束；文中提出训练一个对偶拉格朗日的方式，以拉格朗日乘子权重作为约束的权重去求解最优策略；
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Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning