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Visual Sensor Network Reconfiguration with Deep Reinforcement Learning
lib:48447088155f30ca (v1.0.0)
Authors: Paul Jasek,Bernard Abayowa
ArXiv: 1808.04287
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1808.04287v1
We present an approach for reconfiguration of dynamic visual sensor networks with deep reinforcement learning (RL). Our RL agent uses a modified asynchronous advantage actor-critic framework and the recently proposed Relational Network module at the foundation of its network architecture. To address the issue of sample inefficiency in current approaches to model-free reinforcement learning, we train our system in an abstract simulation environment that represents inputs from a dynamic scene. Our system is validated using inputs from a real-world scenario and preexisting object detection and tracking algorithms.
ArXiv: 1808.04287
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1808.04287v1
We present an approach for reconfiguration of dynamic visual sensor networks with deep reinforcement learning (RL). Our RL agent uses a modified asynchronous advantage actor-critic framework and the recently proposed Relational Network module at the foundation of its network architecture. To address the issue of sample inefficiency in current approaches to model-free reinforcement learning, we train our system in an abstract simulation environment that represents inputs from a dynamic scene. Our system is validated using inputs from a real-world scenario and preexisting object detection and tracking algorithms.
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