  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.
