Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO_x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO_x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

长期以来，传统的压燃式（CI）发动机可在各种负载下提供较高的热效率和扭矩，但通常需要进行大量的废气处理，从而降低效率，以满足日益增长的排放法规。减少排放的一种策略是将燃油喷射分成一系列较小的喷射。在本文中，我们探索了一种使用深度强化学习（DRL）发现下一代CI引擎最佳控制策略的新方法。我们勾勒出一个DRL程序最大化发动机工作时的加权回报，同时最小化终端的循环NO _X排放。通过本文概述的过程，我们表明DRL剂能够还原NO _x排放量增加了三倍，而网络却只减少了2％。我们演示了跨物理模型层次结构使用转移学习（TL）来加速学习过程，从而使该方法对于该空间内的一系列控制问题均可行。本文提供了在技术领域（例如多脉冲发动机控制）中使用DRL设计控制系统的框架和演示，在该领域中，模型层次结构与多目标奖励相结合，可实现最佳运行。