2019/12/03 by Hangyu Mao, Mao, Hangyu, Wulong Liu +14 · 1 citation
Computer Science · Physics and Astronomy · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Cognition #Cognitive science #Complex Network Analysis Techniques #Computer science #Consistency (knowledge bases) #FOS: Computer and information sciences #Human–computer interaction #Mobile Crowdsensing and Crowdsourcing #Psychology #Reinforcement Learning in Robotics #Reinforcement learning #cs.AI
paper · pdf · doi:10.48550/arxiv.1912.01160
Accepted by AAAI2020 with oral presentation (https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf). Since AAAI2020 has started, I have the right to distribute this paper on arXiv
openalex publication_date 2019/12/03 · arxiv created 2020/02/10 · arxiv updated 2020/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation. Meanwhile, only cognitive consistency within a neighborhood matters because humans only interact directly with their neighbors. Inspired by these observations, we take the first step to introduce neighborhood cognitive consistency (NCC) into multi-agent reinforcement learning (MARL). Our NCC design is quite general and can be easily combined with existing MARL methods. As examples, we propose neighborhood cognition consistent deep Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations. Extensive experiments on several challenging tasks (i.e., packet routing, wifi configuration, and Google football player control) justify the superior performance of our methods compared with state-of-the-art MARL approaches.