Investigation into the Performance Enhancement and Configuration Paradigm of Partially Integrated RL-MPC System

The improvement of the partially integrated reinforcement learning-model predictive control (RL-MPC) system is developed in the paper by introducing the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms. This framework differs from the traditional ones, which completely substitute the MPC prediction model; instead, an RL agent refines predictions through feedback correction and thus maintains interpretability while improving robustness. Most importantly, the study details two configuration paradigms: decoupled (offline policy application) and coupled (online policy update) and tests them for their effectiveness in trajectory tracking tasks within simulation and real-life experiments. A decoupled framework based on TD3 showed significant improvements in control performance compared to the rest of the implemented paradigms, especially concerning Integral of Time-weighted Absolute Error (ITAE) and mean absolute error (MAE). This work also illustrated the advantages of partial integration in balancing adaptability and stability, thus making it suitable for real-time applications in robotics.