Integral Reinforcement Learning for Optimal Regulation
摘要
Reinforcement learning (RL) has emerged as a powerful tool for designing feedback controllers for continuous-time (CT) dynamical systems. This approach enables the development of adaptive controllers that can learn optimal control solutions in a forward-in-time manner, even in the absence of complete knowledge about the system dynamics.