标题： 在部分可观测网格中用于可扩展和样本高效的协作任务分配的MARL中的人工通信与涌现通信 显示英文标题

标题： Engineered over Emergent Communication in MARL for Scalable and Sample-Efficient Cooperative Task Allocation in a Partially Observable Grid

摘要： 我们在合作多智能体强化学习（MARL）环境中比较了学习的通信策略与工程的通信策略的有效性。 对于学习的方法，我们引入了学习的直接通信（LDC），其中智能体通过神经网络同时生成消息和动作。 我们的工程方法，意图通信，采用了一个想象轨迹生成模块（ITGM）和一个消息生成网络（MGN），根据预测的未来状态来制定消息。 这两种策略在完全可观测和部分可观测条件下的合作任务中的成功率进行了评估。 我们的研究结果表明，虽然涌现的通信是可行的，但工程方法表现出更好的性能和可扩展性，尤其是在环境复杂性增加时。 显示英文摘要

摘要： We compare the efficacy of learned versus engineered communication strategies in a cooperative multi-agent reinforcement learning (MARL) environment. For the learned approach, we introduce Learned Direct Communication (LDC), where agents generate messages and actions concurrently via a neural network. Our engineered approach, Intention Communication, employs an Imagined Trajectory Generation Module (ITGM) and a Message Generation Network (MGN) to formulate messages based on predicted future states. Both strategies are evaluated on their success rates in cooperative tasks under fully and partially observable conditions. Our findings indicate that while emergent communication is viable, the engineered approach demonstrates superior performance and scalability, particularly as environmental complexity increases.