  Location is key to spatialize internet-of-things (IoT) data. However, it is
challenging to use low-cost IoT devices for robust unsupervised localization
(i.e., localization without training data that have known location labels).
Thus, this paper proposes a deep reinforcement learning (DRL) based
unsupervised wireless-localization method. The main contributions are as
follows. (1) This paper proposes an approach to model a continuous
wireless-localization process as a Markov decision process (MDP) and process it
within a DRL framework. (2) To alleviate the challenge of obtaining rewards
when using unlabeled data (e.g., daily-life crowdsourced data), this paper
presents a reward-setting mechanism, which extracts robust landmark data from
unlabeled wireless received signal strengths (RSS). (3) To ease requirements
for model re-training when using DRL for localization, this paper uses RSS
measurements together with agent location to construct DRL inputs. The proposed
method was tested by using field testing data from multiple Bluetooth 5 smart
ear tags in a pasture. Meanwhile, the experimental verification process
reflected the advantages and challenges for using DRL in wireless localization.
