  Station-keeping tasks for high-altitude balloons show promise in areas such
as ecological surveys, atmospheric analysis, and communication relays. However,
identifying the optimal time and position to launch a latex high-altitude
balloon is still a challenging and multifaceted problem. For example, tasks
such as forest fire tracking place geometric constraints on the launch location
of the balloon. Furthermore, identifying the most optimal location also heavily
depends on atmospheric conditions. We first illustrate how reinforcement
learning-based controllers, frequently used for station-keeping tasks, can
exploit the environment. This exploitation can degrade performance on unseen
weather patterns and affect station-keeping performance when identifying an
optimal launch configuration. Valuing all states equally in the region, the
agent exploits the region's geometry by flying near the edge, leading to risky
behaviours. We propose a modification which compensates for this exploitation
and finds this leads to, on average, higher steps within the target region on
unseen data. Then, we illustrate how Bayesian Optimisation (BO) can identify
the optimal launch location to perform station-keeping tasks, maximising the
expected undiscounted return from a given rollout. We show BO can find this
launch location in fewer steps compared to other optimisation methods. Results
indicate that, surprisingly, the most optimal location to launch from is not
commonly within the target region. Please find further information about our
project at https://sites.google.com/view/bo-lauch-balloon/.
