For decision-making, one common approach first The Conversational Machine Reading (CMR) task segments the document into many text spans at (Saeidi et al., 2018) requires an agent to answer an different granularity levels (e.g., sentences or Elementary initial question from users through multi-turn dialogue Discourse Units (EDUs)). Then complex interactions based on a given document. As modules are adopted to predict the entailment state shown in Figure 1, a typical process involves two for each document span based on user scenario and steps, (1) the agent first makes a decision classification previous dialogue history (both are user-provided among IRRELEVANT, YES, NO and MORE, information). Finally, decisions are made based on (2) if the decision is MORE, the agent generates a the entailment states of all document spans. One question to clarify an unmentioned condition in the effective module for predicting entailment states is given document, otherwise responds directly. Recent transformer blocks (Vaswani et al., 2017), which research (Verma et al., 2020; Lawrence et al., are widely adopted (Gao et al., 2020b; Ouyang 2019; Zhong and Zettlemoyer, 2019; Gao et al., et al., 2021; Zhang et al., 2022). However, the 2020a; Gao et al., 2020b; Ouyang et al., 2021; aforementioned approach has overlooked the explicit Zhang et al., 2022) has explored how to improve alignment between the document and the userprovided the abilities of decision-making and question generation.

The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance. However, in the context of smart manufacturing in the process industry, traditional model-based optimization control methods are unable to adapt to the varying operating conditions of the ORC system or sudden changes in operating modes. Deep reinforcement learning (DRL) has significant advantages in situations with uncertainty as it directly achieves control objectives by interacting with the environment without requiring an explicit model of the controlled plant. Nevertheless, direct application of DRL to physical ORC systems presents unacceptable safety risks, and its generalization performance under model-plant mismatch is insufficient to support ORC control requirements. Therefore, this paper proposes a Sim2Real transfer learning-based DRL control method for ORC superheat control, which aims to provide a new simple, feasible, and user-friendly solution for energy system optimization control. Experimental results show that the proposed method greatly improves the training speed of DRL in ORC control problems and solves the generalization performance issue of the agent under multiple operating conditions through Sim2Real transfer.