Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/135896
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Type: Conference paper
Title: Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation
Author: Kazemi Moghaddam, M.
Wu, Q.
Abbasnejad, E.
Shi, J.
Citation: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021), 2021, pp.3732-3741
Publisher: IEEE
Publisher Place: online
Issue Date: 2021
Series/Report no.: IEEE Winter Conference on Applications of Computer Vision
ISBN: 9780738142661
ISSN: 2472-6737
Conference Name: IEEE Winter Conference on Applications of Computer Vision (WACV) (5 Jan 2021 - 9 Jan 2021 : virtual online)
Statement of
Responsibility: 
Mahdi Kazemi Moghaddam, Qi Wu, Ehsan Abbasnejad and Javen Shi
Abstract: We humans can impeccably search for a target object, given its name only, even in an unseen environment. We argue that this ability is largely due to three main reasons: the incorporation of prior knowledge (or experience), the adaptation of it to the new environment using the observed visual cues and most importantly optimistically searching without giving up early. This is currently missing in the state-of-the-art visual navigation methods based on Reinforcement Learning (RL). In this paper, we propose to use externally learned prior knowledge of the relative object locations and integrate it into our model by constructing a neural graph. In order to efficiently incorporate the graph without increasing the state-space complexity, we propose Graph-based Value Estimation (GVE) module. GVE provides a more accurate baseline for estimating the Advantage function in actor-critic RL algorithm. This results in reduced value estimation error and, consequently, convergence to a more optimal policy. Through empirical studies, we show that our agent, dubbed as the optimistic agent, has a more realistic estimate of the state value during a navigation episode which leads to a higher success rate. Our extensive ablation studies show the efficacy of our simple method which achieves the state-of-the-art results measured by the conventional visual navigation metrics, e.g. Success Rate (SR) and Success weighted by Path Length (SPL), in AI2THOR environment.
Keywords: cs.CV; cs.CV
Rights: ©2021 IEEE
DOI: 10.1109/WACV48630.2021.00378
Published version: https://ieeexplore.ieee.org/xpl/conhome/9423008/proceeding
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Computer Science publications

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