The field of control systems and reinforcement learning is moving towards more complex and realistic applications, with a focus on energy efficiency, optimal performance, and automated environment design. Researchers are exploring the use of multi-objective reinforcement learning and automated environment design to improve the performance of control systems in various domains, including industrial automation and power flow optimization. The development of new frameworks and algorithms is enabling the application of reinforcement learning to more challenging problems, such as the control of production lines and high-altitude test stands. Noteworthy papers include:

• The introduction of the LineFlow framework, which provides a standardized and general framework for simulating production lines and training reinforcement learning agents to control them.
• The proposal of a general approach for automated reinforcement learning environment design using multi-objective optimization, which has been shown to outperform manual environment design.
• The development of a coordinated multi-valve disturbance-rejection pressure control scheme for high-altitude test stands, which has demonstrated superior disturbance rejection and decoupling performance.