How is reinforcement learning applied to control systems?

Reinforcement learning (RL) learns control policies by interacting with the environment and receiving rewards, without requiring an explicit model. Policy optimization directly learns control law; value-based methods learn value function then derive policy. Deep RL uses neural networks for function approximation in high-dimensional spaces. Applications include robotics manipulation, autonomous driving, and HVAC optimization. Challenges include sample efficiency (many interactions needed), safety during learning (exploration may cause damage), stability guarantees, and sim-to-real transfer. Combines with model-based methods for improved efficiency.