Improving wet clutch engagement with reinforcement learning

A common approach when applying reinforcement learning to address control problems is that of first learning a policy based on an approximated model of the plant, whose behavior can be quickly and safely explored in simulation; and then implementing the obtained policy to control the actual plant. Here we follow this approach to learn to engage a transmission clutch, with the aim of obtaining a rapid and smooth engagement, with a small torque loss. Using an approximated model of a wet clutch, which simulates a portion of the whole engagement, we first learn an open loop control signal, which is then transferred on the actual wet clutch, and improved by further learning with a different reward function, based on the actual torque loss observed.