间歇过程控制中的双行动者双延迟深度确定性策略梯度（TATD3）学习
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control
摘要：
间歇过程具有复杂的非线性动力学和非稳态操作条件，其控制是一项困难的任务。预计其中一些挑战可以通过制定与该过程直接互动的控制战略和从经验中学习来解决。最近的文献研究表明，在行动者-批评者强化学习（RL）框架中拥有一群行动者的优势，可以改善政策。针对连续控制问题，在已有的双延迟深度确定性策略梯度（TD 3）算法基础上，引入双作用器网络，提出了一种双作用器双延迟深度确定性策略梯度（TATD 3）强化学习算法.此外，还针对TATD 3控制器提出了两种新型的奖励函数。我们展示了TATD 3为基础的控制器的各种批处理的例子，通过比较它与文献中提出的一些现有的RL算法的功效。
英文摘要：
Control of batch processes is a difficult task due to their complex nonlinear dynamics and unsteady-state operating conditions within batch and batch-to-batch. It is expected that some of these challenges can be addressed by developing control strategies that directly interact with the process and learning from experiences. Recent studies in the literature have indicated the advantage of having an ensemble of actors in actor-critic Reinforcement Learning (RL) frameworks for improving the policy. The present study proposes an actor-critic RL algorithm, namely, twin actor twin delayed deep deterministic policy gradient (TATD3), by incorporating twin actor networks in the existing twin-delayed deep deterministic policy gradient (TD3) algorithm for the continuous control. In addition, two types of novel reward functions are also proposed for TATD3 controller. We showcase the efficacy of the TATD3 based controller for various batch process examples by comparing it with some of the existing RL algorithms presented in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
