  Indoor positioning using UWB technology has gained interest due to its
centimeter-level accuracy potential. However, multipath effects and
non-line-of-sight conditions cause ranging errors between anchors and tags.
Existing approaches for mitigating these ranging errors rely on collecting
large labeled datasets, making them impractical for real-world deployments.
This paper proposes a novel self-supervised deep reinforcement learning
approach that does not require labeled ground truth data. A reinforcement
learning agent uses the channel impulse response as a state and predicts
corrections to minimize the error between corrected and estimated ranges. The
agent learns, self-supervised, by iteratively improving corrections that are
generated by combining the predictability of trajectories with filtering and
smoothening. Experiments on real-world UWB measurements demonstrate comparable
performance to state-of-the-art supervised methods, overcoming data dependency
and lack of generalizability limitations. This makes self-supervised deep
reinforcement learning a promising solution for practical and scalable
UWB-ranging error correction.
