Jin Yang

Papers

Total Citations

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About

Jin Yang is an emerging researcher specializing in multi-agent reinforcement learning (MARL) and cooperative robotics systems. Their most notable work, "Strengthening Cooperative Consensus in Multi-Robot Confrontation" (2023), addresses a critical gap in how joint action policies are designed and evaluated in competitive multi-robot environments, including applications such as StarCraft simulations and robot soccer games. Yang's research focuses on identifying and mitigating failure-prone actions within collaborative agent frameworks — a challenge that has long hindered the practical deployment of MARL systems in dynamic, adversarial settings. By tackling the problem of consensus-building among multiple agents under confrontational conditions, Yang contributes meaningfully to the broader goal of making autonomous robotic teams more reliable and strategically coherent. This work, already accumulating 5 citations within its first year of publication, signals growing interest from the robotics and AI communities in Yang's approach. As MARL continues to expand into real-world applications — from autonomous vehicles to logistics — Yang's contributions to cooperative decision-making and failure prevention position them as a promising voice in next-generation intelligent systems research.