1 code implementation • 16 Nov 2020 • Christopher D. Hsu, Heejin Jeong, George J. Pappas, Pratik Chaudhari
Our method can handle an arbitrary number of pursuers and targets; we show results for tasks consisting up to 1000 pursuers tracking 1000 targets.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 17 Jun 2020 • Heejin Jeong, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas
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.
2 code implementations • 23 Oct 2019 • Heejin Jeong, Brent Schlotfeldt, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas
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.
1 code implementation • 9 Dec 2017 • Heejin Jeong, Clark Zhang, George J. Pappas, Daniel D. Lee
We formulate an efficient closed-form solution for the value update by approximately estimating analytic parameters of the posterior of the Q-beliefs.