Critical infrastructures such as cyber‐physical energy systems (CPS‐E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS‐E is critical to ensure societal well‐being and economic prosperity. Automated control is key for real‐time operations and may be mathematically cast as a sequential decision‐making problem under uncertainty. Emergence of data‐driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision‐making problems for risk‐based robust CPS‐E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS‐E applications, addressing the effect of risk preferences across multiple RL methods, and development of open‐source domain‐aware simulation environments for RL experimentation within a CPS‐E context. This article systematically analyzes the applicability of four types of RL methods (model‐free, model‐based, hybrid model‐free and model‐based, and hierarchical) for risk‐based robust CPS‐E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk‐averse, risk‐neutral, and risk‐seeking) through the development and application of an open‐source simulation environment. Motivating numerical simulation examples include representative single‐zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

The doubly fed induction generator (DFIG) usually experiences high rotor current and DC capacitor link voltage spikes during system fault events. In this paper, a novel data‐driven approach is proposed to enhance DFIG performance under fault scenarios. An advanced reinforcement learning algorithm called guided surrogate‐gradient‐based evolution strategy (GSES) is used to control the DFIG power and capacitor DC‐link voltage by adjusting the optimal reference signals. This controller is able to prevent the DFIG rotor from over‐current risk and maintain grid‐connected operation. The proposed GSES‐based control algorithm was evaluated through simulations on a 3.6‐MW DFIG in the PSCAD/EMTDC software. Results have validated the effectiveness of the proposed GSES‐based control algorithm in improving DFIG performance under various fault scenarios.