Search Results for author: Heejin Jeong

Found 4 papers, 4 papers with code

Scalable Reinforcement Learning Policies for Multi-Agent Control

1 code implementation16 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

Learning to Track Dynamic Targets in Partially Known Environments

1 code implementation17 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.

Navigate

Learning Q-network for Active Information Acquisition

2 code implementations23 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.

reinforcement-learning

Assumed Density Filtering Q-learning

1 code implementation9 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.

Atari Games Bayesian Inference +1

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