For the localization of multiple users, Bluetooth data from the smartphone is able to complement Wi-Fi-based methods with additional information, by providing an approximation of the relative distances between users. In practice, both positions provided by Wi-Fi data and relative distance provided by Bluetooth data are subject to a certain degree of noise due to the uncertainty of radio propagation in complex indoor environments. In this study, we propose and evaluate two approaches, namely Non-temporal and Temporal ones, of collaborative positioning to combine these two cohabiting technologies to improve the tracking performance. In the Non-temporal approach, our model establishes an error observation function in a specific interval of the Bluetooth and Wi-Fi output. It is then able to reduce the positioning error by looking for ways to minimize the error function. The Temporal approach employs an extended error model that takes into account the time component between users’ movements. For performance evaluation, several multi-user scenarios in an indoor environment are set up. Results show that for certain scenarios, the proposed approaches attain over 40% of improvement in terms of average accuracy.

Soft actor-critic (SAC) is a reinforcement learning algorithm that employs the maximum entropy framework to train a stochastic policy. This work examines a specific failure case of SAC where the stochastic policy is trained to maximize the expected entropy from a sparse reward environment. We demonstrate that the over-exploration of SAC can make the entropy temperature collapse, followed by unstable updates to the actor. Based on our analyses, we introduce Reg-SAC, an improved version of SAC, to mitigate the detrimental effects of the entropy temperature on the learning stability of the stochastic policy. Reg-SAC incorporates a clipping value to prevent the entropy temperature collapse and regularizes the gradient updates of the policy via Kullback-Leibler divergence. Through experiments on discrete benchmarks, our proposed Reg-SAC outperforms the standard SAC in spare-reward grid world environments while it is able to maintain competitive performance in the dense-reward Atari benchmark. The results highlight that our regularized version makes the stochastic policy of SAC more stable in discrete-action domains.