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Integrating Distributed Bayesian Inference and Reinforcement Learning for Sensor Management

Corrado Grappiolo‚ Shimon Whiteson‚ Gregor Pavlin and Bram Bakker


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.

Book Title
FUSION 2009: Proceedings of the Twelfth International Conference on Information Fusion