Deep Reinforcement Learning for Control of Fuel Injection in Compression Ignition Engines

Compression ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but come at the cost of high quantities of NOx and soot. One strategy to decrease harmful emissions from CI engines is to split the fuel injection into a series of smaller injections. In this talk, we explore a new way of discovering optimal injection strategies for the next generation of compression ignition engines using deep reinforcement learning (DRL). An overview of the DRL algorithm and training procedure are outlined and the resulting new injection schedules are discussed. 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 complex scientific problems. Using a well-trained DRL agent as a controller, NOx emissions from a zero-dimensional model are reduced three-fold while only decreasing net work by 2\%.