  In the realm of circuit motorsports, race strategy plays a pivotal role in
determining race outcomes. This strategy focuses on the timing of pit stops,
which are necessary due to fuel consumption and tire performance degradation.
The objective of race strategy is to balance the advantages of pit stops, such
as tire replacement and refueling, with the time loss incurred in the pit lane.
Current race simulations, used to estimate the best possible race strategy,
vary in granularity, modeling of probabilistic events, and require manual input
for in-laps. This paper addresses these limitations by developing a novel
simulation model tailored to GT racing and leveraging artificial intelligence
to automate strategic decisions. By integrating the simulation with OpenAI's
Gym framework, a reinforcement learning environment is created and an agent is
trained. The study evaluates various hyperparameter configurations, observation
spaces, and reward functions, drawing upon historical timing data from the 2020
N\"urburgring Langstrecken Serie for empirical parameter validation. The
results demonstrate the potential of reinforcement learning for improving race
strategy decision-making, as the trained agent makes sensible decisions
regarding pit stop timing and refueling amounts. Key parameters, such as
learning rate, decay rate and the number of episodes, are identified as crucial
factors, while the combination of fuel mass and current race position proves
most effective for policy development. The paper contributes to the broader
application of reinforcement learning in race simulations and unlocks the
potential for race strategy optimization beyond FIA Formula~1, specifically in
the GT racing domain.
