Fast and low-complexity reinforcement learning for delay-sensitive energy harvesting wireless visual sensing systems
Fast and low-complexity reinforcement learning for delay-sensitive energy harvesting wireless visual sensing systems
复制标题
用于延迟敏感能量收集无线视觉传感系统的快速、低复杂度强化学习
DOI：
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发表时间：
2016
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通讯作者：
Nicholas Mastronarde
中科院分区：
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作者：
Niloofar Toorchi;Jacob Chakareski;Nicholas Mastronarde
在本文中，我们考虑了能源挑战的遥感器传输延迟敏感的图像数据在一个随时间变化的信道。传感器从环境中收集能量，因此有效的能量消耗非常重要。在本文中，我们的目标是找到最佳的传输调度和电源管理政策，最大限度地提高可用能量为未来的传输，同时满足排队延迟约束。我们将这个问题表示为马尔可夫决策过程（MDP），并提出了一种强化学习（RL）算法来在线解决这个问题。我们的实验表明，该算法实现了相当的性能，一个国家的最先进的RL算法，但在低得多的复杂度。
In this paper, we consider an energy challenged remote sensor transmitting latency-sensitive imagery data over a time-varying channel. The sensor harvests energy from the environment and hence efficient energy consumption is of great importance. In this paper, we aim to find the optimal transmission scheduling and power management policies that maximize the available energy for future transmissions while meeting a queuing delay constraint. We formulate this problem as a Markov Decision Process (MDP) and propose a reinforcement learning (RL) algorithm to solve it online. Our experiments show that the proposed algorithm achieves comparable performance to a state-of-the-art RL algorithm, but at much lower complexity.