In traffic systems, central controllers dictate signals while vehicles adapt accordingly. This is especially critical in environments like smart grids or traffic control systems, where conditions change rapidly and resources are often limited. The framework allows for a more robust, adaptive and scalable form of AI control that can make better use of limited bandwidth and computing resources. The approach combines deep control theory with practical machine learning, offering a compelling path forward for intelligent control in asymmetric, uncertain environments. More information: Xiangnan Zhong et al, Intelligent Control in Asymmetric Decision-Making: An Event-Triggered RL Approach for Mismatched Uncertainties, IEEE Transactions on Systems, Man, and Cybernetics: Systems (2025).