  The reconstruction of charged particle trajectories is a crucial challenge of
particle physics experiments as it directly impacts particle reconstruction and
physics performances. To reconstruct these trajectories, different
reconstruction algorithms are used sequentially. Each of these algorithms uses
many configuration parameters that must be fine-tuned to properly account for
the detector/experimental setup, the available CPU budget and the desired
physics performance. Examples of such parameters are cut values limiting the
algorithm's search space, approximations accounting for complex phenomenons, or
parameters controlling algorithm performance. Until now, these parameters had
to be optimised by human experts, which is inefficient and raises issues for
the long-term maintainability of such algorithms. Previous experience using
machine learning for particle reconstruction (such as the TrackML challenge)
has shown that they can be easily adapted to different experiments by learning
directly from the data. We propose to bring the same approach to the classic
track reconstruction algorithms by connecting them to an agent-driven
optimiser, allowing us to find the best input parameters using an iterative
tuning approach. We have so far demonstrated this method on different track
reconstruction algorithms within A Common Tracking Software (ACTS) framework
using the Open Data Detector (ODD). These algorithms include the trajectory
seed reconstruction and selection, the particle vertex reconstruction and the
generation of simplified material maps used for trajectory reconstruction.
