RL Control

Reinforcement learning control studies how agents can learn actions that steer dynamic systems toward goals while managing costs, constraints, and uncertainty. Common approaches include actor-critic methods such as PPO, SAC, and TD3; model-based learning with predictive control; and offline learning from logged data, often combined with safety filters or physical models. Work spans simulated benchmarks and applications such as robotics, traffic, buildings, power grids, and fluid flow, with evaluation increasingly testing hardware transfer and changing conditions. A central tension is that high simulated reward or average performance does not ensure safe, reliable behavior under real-world disturbances, limited data, or distribution shift.