the formulation of a linear quadratic regulator (lqr) for a system characterized by partial differential equations (pde) utilizing reinforcement learning technique

Reinforcement learning has emerged as a valuable tool in control theory and related areas, such as robotics and process control, facilitating prediction, identification, and management of complex systems. a key advantage of using reinforcement learning is its ability to derive optimal control policies without requiring a comprehensive understanding of the underlying system dynamics. in essence, rl develops control strategies through interaction with the environment. a common approach in controller design for these systems involves the use of approximation methods. given the complexities associated with solving pdes analytically, numerical techniques like the finite element method are typically employed for approximation. in this research, we begin by discretizing the pde that characterizes the system's dynamics using an appropriate discretization technique. following this step, we extract the discrete dynamics of the system. since these discrete dynamics exhibit the markov property where the future state depends only on the current state and the action taken the next phase involves designing a controller based on these derived dynamics. recognizing that reinforcement learning focuses on optimizing future actions through data analysis, feedback from the optimal mode can be utilized as a viable option for controller design. this study will further explore lqr controller design for one-dimensional heat equation heat flow as a system whose dynamic is described with pde.