将强化学习方法应用到离合器的接合控制，旨在获得快速平稳的接合，伴随着较小的转矩损失。(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 exp

在许多工业应用场合，安置传感器测量系统的输出是不可能或非常昂贵的。此外，物体的运动仅能在一些离散点被检测到。在这些系统中，控制目标通常不是跟踪正确的轨迹，而是在传感器的位置获得给定的状态，在这篇论文中，引入RL学习策略来控制湿式离合器，以实现快速的接合。(In many industrial applications, it is impossible or very expensive to place sensor measurement system output. In addition