This thesis presents a new tuning strategy for the node-level auto-tuned particle simulation
library AutoPas. The strategy uses reinforcement learning to predict the best configuration
for the simulation to use to achieve the fastest calculation time. An implementation of a
modified version of the SARSA algorithm is shown. Furthermore, the hyperparameters:
learning rate, discount factor, and exploration rate are fine-tuned trough grid search to
produce the best possible results. The reinforcement learning tuning strategy is then tested
according to different criteria. These criteria are then used to compare it against the full
search and predictive tuning strategies. The reinforcement learning tuning interface shows
considerable improvement compared to the already implemented options.