  Underwater target localization using range-only and single-beacon (ROSB)
techniques with autonomous vehicles has been used recently to improve the
limitations of more complex methods, such as long baseline and ultra-short
baseline systems. Nonetheless, in ROSB target localization methods, the
trajectory of the tracking vehicle near the localized target plays an important
role in obtaining the best accuracy of the predicted target position. Here, we
investigate a Reinforcement Learning (RL) approach to find the optimal path
that an autonomous vehicle should follow in order to increase and optimize the
overall accuracy of the predicted target localization, while reducing time and
power consumption. To accomplish this objective, different experimental tests
have been designed using state-of-the-art deep RL algorithms. Our study also
compares the results obtained with the analytical Fisher information matrix
approach used in previous studies. The results revealed that the policy learned
by the RL agent outperforms trajectories based on these analytical solutions,
e.g. the median predicted error at the beginning of the target's localisation
is 17% less. These findings suggest that using deep RL for localizing acoustic
targets could be successfully applied to in-water applications that include
tracking of acoustically tagged marine animals by autonomous underwater
vehicles. This is envisioned as a first necessary step to validate the use of
RL to tackle such problems, which could be used later on in a more complex
scenarios
