Integrating Reinforcement Learning and Distributed Perception Networks for Mobile Sensor Control

This thesis describes an approach that uses reinforcement learning together with distributed perception network fusion systems in order to perform mobile sensor control. A case study of such mobile sensors is the chemical leak detection problem. The proposed solution deals with partial observability of the true state and makes use of linear function approximation to learn a value function that maps belief-action pairs into values, distributed perception networks to create a correct, robust and computationally efficient system for the inference of gas leak given sensor observations, and bayesian inference to estimate the leak location. Results show that by keeping a continuous belief represented through entropies and representing information about both single cell and aggregation of cells, the system can learn successfully; furthermore, studies about the influence that the distributed perception networks give to the whole system are done. Discussions and future developments are also provided.