# Target State Optimization: Drivability Improvement for Vehicles with Dual Clutch Transmissions
## 1 Introduction
The RL algorithm called agent is a computational approach mimicking the learning behavior of creatures by making machines interact with an environment. The agent takes actions to influence the environment and the environment responds with a state it takes in after receiving the action. The state is fed back to the agent on which basis the next action is chosen to influence the environment towards a defined goal (which is related to the state). The quality of an action is measured with the reward. With the reward, the agent learns which actions are preferably to take. An action which leads to an environmental state towards an optimization goal is rewarded higher than a contrary one. Therefore, the aim is to increase the reward with actions of higher quality [32]. Usually, RL algorithms are used to solve combinatorial problems like the traveling salesman problem [34; 35]. Nevertheless, the improvement of clutch engagement with RL has also been in the scope of research. [PERSON] et al. [36] tried to minimize the jerk and the friction losses for clutch engagement during launch within simulations by varying the parameters of a PID controller with a RL algorithm. Another approach was the minimization of the piston velocity and the engagement time of the clutch by [PERSON] et al. [37] and [PERSON] et al. [38] applying a RL algorithm. Their approach led to promising results after a few hundred epochs. The optimization problem of [PERSON] et al. [39] is similar to the work of [PERSON] et al. [37] and [PERSON] et al. [38]. Within a simulation, a RL algorithm is applied. The study focuses on multi-objective aspects and on the effects of scalarization on the performance of the algorithm. The optimization of the quality of the clutch engagement with a RL algorithm, while ensuring an immediate response, is investigated by [PERSON] et al. [40]. The optimization of the calibration parameters has not been part of the studies which focused on clutch engagement. One possible reason is that the calibration problem is not a combinatorial one; another could be the fact that RL needs many optimization epochs to converge, as illustrated in [37; 41]. The drawbacks of RL and GA approaches in the optimization of the launch behavior through optimizing calibration parameters makes these approaches difficult to use in the regular development process of dual clutch transmissions. To overcome these issues, the TSO algorithm by [PERSON] et al. [1] is applied for the calibration problem. The TSO algorithm is also based on machine learning (ML) as a hybrid approach of RL and supervised learning (SL). The application of the TSO algorithm increases the efficiency of the calibration process within different test environments. This paper aims to dive deeper in the TSO algorithm and illustrates the influence of the activation functions of the used neural networks on optimization behavior. Further, it illustrates a significant advantage compared to previous approaches applied for such optimization problems, but also mentions the drawbacks of the algorithm. The optimization problem and the results of the subject test studies are described in Section 2. In Section 3 the results of existing benchmark algorithms are illustrated. The TSO algorithm is explained in Section 4. The results are outlined in Section 5. Section 6 discusses and interprets the results and finally, in Section 7, the conclusion is presented.