Learning Q-network for Active Information Acquisition
This work addresses the challenge of model dependence and horizon limitations in information acquisition for robotic systems, offering a novel but incremental improvement over traditional planning methods.
The paper tackles the Active Information Acquisition problem by proposing a Reinforcement Learning approach that eliminates the need for known models and handles long planning horizons, resulting in policies efficient for real-time robotic control. It demonstrates performance comparable to existing methods in multi-target tracking scenarios.
In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors. The classic challenges in the information acquisition problem are the dependence of a planning algorithm on known models and the difficulty of computing information-theoretic cost functions over arbitrary distributions. In contrast, the proposed framework of reinforcement learning does not require any knowledge on models and alleviates the problems during an extended training stage. It results in policies that are efficient to execute online and applicable for real-time control of robotic systems. Furthermore, the state-of-the-art planning methods are typically restricted to short horizons, which may become problematic with local minima. Reinforcement learning naturally handles the issue of planning horizon in information problems as it maximizes a discounted sum of rewards over a long finite or infinite time horizon. We discuss the potential benefits of the proposed framework and compare the performance of the novel algorithm to an existing information acquisition method for multi-target tracking scenarios.