This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically discover a mapping from the beliefs generated by the DPNs to the actions that enable active sensors to gather the most useful observations. The resulting method is evaluated on a simulation of a chemical leak localization task and the results demonstrate 1) that the integrated approach can learn policies that perform effective sensor management, 2) that inference based on a correct observation model, which the DPNs make feasible, is critical to performance, and 3) that the system scales to larger versions of the task.
['Corrado Grappiolo', 'Shimon Whiteson', 'Gregor Pavlin', 'Bram Bakker']
Integrating distributed Bayesian inference and reinforcement learning for sensor management