混合人格改进的脉冲演员网络用于高效多智能体协作
Mixture of personality improved Spiking actor network for efficient multi-agent cooperation
AI总结：
受人格理论启发，提出混合人格改进的脉冲演员网络（MoP-SAN），利用行列式点过程模拟人格形成，在Overcooked任务中实现高效多智能体协作与良好泛化。
摘要：
自适应的人机协作和智能体间协作在多智能体强化学习（MARL）研究领域变得越来越关键，在深度神经网络的帮助下已取得了显著进展。然而，许多现有算法在学习阶段表现良好，但在与其他未见过的伙伴协作时泛化能力较差。认知心理学中的人格理论描述，人类通过首先预测他人的人格，然后预测其复杂行为，能够很好地应对上述协作挑战。受这一两步心理学理论的启发，我们提出了一种生物上合理的混合人格（MoP）改进的脉冲演员网络（SAN），其中使用行列式点过程来模拟MoP中不同类型人格的复杂形成与整合，并将动态和脉冲神经元纳入SAN以实现高效的强化学习。选择了对协作烹饪有强烈要求的基准Overcooked任务来测试所提出的MoP-SAN。实验结果表明，MoP-SAN不仅在学习阶段，而且在泛化测试（即与其他未见过的智能体协作）阶段都能取得高性能，而大多数对应的深度演员网络在泛化测试中失败。进行了必要的消融实验和可视化分析，以解释为什么MoP和SAN在多智能体强化学习场景中有效，而DNN在泛化测试中表现不佳。
英文摘要：
Adaptive human-agent and agent-agent cooperation are becoming more and more critical in the research area of multi-agent reinforcement learning (MARL), where remarked progress has been made with the help of deep neural networks. However, many established algorithms can only perform well during the learning paradigm but exhibit poor generalization during cooperation with other unseen partners. The personality theory in cognitive psychology describes that humans can well handle the above cooperation challenge by predicting others' personalities first and then their complex actions. Inspired by this two-step psychology theory, we propose a biologically plausible mixture of personality (MoP) improved spiking actor network (SAN), whereby a determinantal point process is used to simulate the complex formation and integration of different types of personality in MoP, and dynamic and spiking neurons are incorporated into the SAN for the efficient reinforcement learning. The benchmark Overcooked task, containing a strong requirement for cooperative cooking, is selected to test the proposed MoP-SAN. The experimental results show that the MoP-SAN can achieve both high performances during not only the learning paradigm but also the generalization test (i.e., cooperation with other unseen agents) paradigm where most counterpart deep actor networks failed. Necessary ablation experiments and visualization analyses were conducted to explain why MoP and SAN are effective in multi-agent reinforcement learning scenarios while DNN performs poorly in the generalization test.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
