Critical Reinforcement Learning Of Actors Drives Decision-making In Energy System Optimizationsteam Injection Optimization

Machine learning techniques, especially reinforcement learning, have been applied to dynamic optimization tasks in recent years. The primary goals of these approaches are to develop control policies that allow efficient mapping of sensor inputs to actuation outputs and to support real-time decision making in complex environments. Reinforcement learning allows agents to learn from their interactions with the environment and adapt their behavior accordingly, making it well suited to deal with high-dimensional nonlinear optimization problems.