Accelerating reinforcement learning with case-based model-assisted experience augmentation for process control

In the context of intelligent manufacturing in the process industry, traditional model-based optimization control methods cannot adapt to the situation of drastic changes in working conditions or operating modes. Reinforcement learning (RL) directly achieves the control objective by interacting with the environment, and has significant advantages in the presence of uncertainty since it does not require an explicit model of the operating plant. However, most RL algorithms fail to retain transfer learning capabilities in the presence of mode variation, which becomes a practical obstacle to industrial process control applications. To address these issues, we design a framework that uses local data augmentation to improve the training efficiency and transfer learning (adaptability) performance. Therefore, this paper proposes a novel RL control algorithm, CBR-MA-DDPG, organically integrating case-based reasoning (CBR), model-assisted (MA) experience augmentation, and deep deterministic policy gradient (DDPG). When the operating mode changes, CBR-MA-DDPG can quickly adapt to the varying environment and achieve the desired control performance within several training episodes. Experimental analyses on a continuous stirred tank reactor (CSTR) and an organic Rankine cycle (ORC) demonstrate the superiority of the proposed method in terms of both adaptability and control performance/robustness. The results show that the control performance of the CBR-MA-DDPG agent outperforms the conventional PI and MPC control schemes, and that it has higher training efficiency than the state-of-the-art DDPG, TD3, and PPO algorithms in transfer learning scenarios with mode shift situations.