Human-in-the-Loop Robotic Process Automation (RPA) with Deep Reinforcement Learning (DRL)

In this article, the authors attempt to extend human feedback as a critical decision-making tool to improve DRL for RPA systems, especially in application domains sensitive to change, such as healthcare and legal practice. The previous methods of implementing RPA moved in well-defined trajectories, meaning they have low adaptability for complex decision-making or other atypical cases. These systems can integrate DRL and hence can learn and evolve over the period required. Nevertheless, DRL models often work independently from human beings. They are used in applications with low human interaction, which can cause issues in decision-making procedures that require a higher understanding of regulation.
The article puts human-in-the-loop, with human experts returning to the DRL models. It encapsulates human feedback into meaningful rewards or penalties for the DRL algorithms, ensuring correction and improvement of decision-making brought about by the expert’s input. This approach can enhance the management of exceptions/corner cases typical to regulated domains, the automation of which must consider compliance and legal requirements.
The paper focuses on how human intervention can be combined in DRL to augment the reward schemes that are superior to those provided by purely automated systems to human overseers. The efficiency of the hybrid system of DRL coupled with RPA in real-world use cases that involve processing legal documents and medical records is well supported. Currently, feedback from people in DRL for RPA has the potential to create more dynamic, dependable, and adherent robotic processes in various stringent compliance contexts.