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Adaptive human-agent and agent-agent cooperation are becoming more and morecritical 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 learningparadigm but exhibit poor generalization during cooperation with other unseenpartners. The personality theory in cognitive psychology describes that humanscan well handle the above cooperation challenge by predicting others'personalities first and then their complex actions. Inspired by this two-steppsychology theory, we propose a biologically plausible mixture of personality(MoP) improved spiking actor network (SAN), whereby a determinantal pointprocess is used to simulate the complex formation and integration of differenttypes of personality in MoP, and dynamic and spiking neurons are incorporatedinto the SAN for the efficient reinforcement learning. The benchmark Overcookedtask, containing a strong requirement for cooperative cooking, is selected totest the proposed MoP-SAN. The experimental results show that the MoP-SAN canachieve both high performances during not only the learning paradigm but alsothe generalization test (i.e., cooperation with other unseen agents) paradigmwhere most counterpart deep actor networks failed. Necessary ablationexperiments and visualization analyses were conducted to explain why MoP andSAN are effective in multi-agent reinforcement learning scenarios while DNNperforms poorly in the generalization test.

Mixture of personality improved Spiking actor network for efficient multi-agent cooperation