  We solve active target tracking, one of the essential tasks in autonomous
systems, using a deep reinforcement learning (RL) approach. In this problem, an
autonomous agent is tasked with acquiring information about targets of
interests using its onboard sensors. The classical challenges in this problem
are system model dependence and the difficulty of computing
information-theoretic cost functions for a long planning horizon. RL provides
solutions for these challenges as the length of its effective planning horizon
does not affect the computational complexity, and it drops the strong
dependency of an algorithm on system models. In particular, we introduce Active
Tracking Target Network (ATTN), a unified RL policy that is capable of solving
major sub-tasks of active target tracking -- in-sight tracking, navigation, and
exploration. The policy shows robust behavior for tracking agile and anomalous
targets with a partially known target model. Additionally, the same policy is
able to navigate in obstacle environments to reach distant targets as well as
explore the environment when targets are positioned in unexpected locations.
