Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping
This work introduces Feasible Action for Optimal Control (FAOC), a new framework combining Reinforcement Learning (RL) and Optimal Control (OC). It creates a mapping that translates an RL agent's abstract action into a specific set of parameters guaranteed to be feasible for the OCP. This ensures the RL agent only selects actions that can actually solve the control problem, solving feasibility and exploration issues.