Apprenticeship learning in cognitive jamming

Zhuansun Shaoshuai; Yang Jun‐an; Liu Hui

摘要

Summary >In an electronic warfare–type scenario with a partially known environment, the advantages of trial and error in the reinforcement learning algorithm adopted by a cognitive jammer can disrupt communications between transmitter‐receiver pairs adaptively and optimally. However, complete information regarding the Markov decision process and its thousands of interactions seem to be unreliable, especially in applications that require timeliness, such as cognitive jamming. Faced with uncooperative or hostile transmitter‐receiver pairs, it is a challenging control problem for the jammer to select the proper actions to realize communication denials. This paper addresses apprenticeship learning in cognitive jamming, where inverse reinforcement learning is used by the jammer to acquire skilled behaviors from expert demonstrations. Without a known prior reward function, the jammer can obtain high jamming performance under the guidance of the given expert strategy. More specifically, the number of iterations needed in apprenticeship learning is much less than that needed in Q learning, which is a vital advantage in a fast‐changing environment. Numerous results demonstrate that it is feasible and realistic to use apprenticeship learning to learn the jamming strategy since its performance meets or exceeds those of existing methods. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“en”> <标题类型=“main”>摘要</ title> 在具有部分已知的环境的电子战型方案中，认知干扰器采用的增强学习算法中的试验和误差的优点可以自适应和最佳地扰乱发射机接收器对之间的通信。然而，有关马尔可夫决策过程的完整信息及其数千个交互似乎是不可靠的，尤其是在需要及时性的应用中，例如认知卡住。面对不合作或敌对的发射器 - 接收器对，这对于干扰器来说是一个具有挑战性的控制问题，以选择实现沟通否定的适当动作。本文涉及在认知干扰中的学徒学习，干扰器使用逆钢筋学习，从专家演示中获取熟练的行为。如果没有已知的先前奖励功能，干扰器可以根据给定的专家战略的指导获得高干扰性能。更具体地，学徒学习所需的迭代次数远低于Q学习所需的次数，这在快速变化的环境中是一个重要的优势。许多结果表明，自从其性能符合或超过现有方法的情况以来，使用学徒学习来学习干扰策略是可行和现实的。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-27675/'>《Optimal Control Applications and Methods》</a> <b style="margin: 0 2px;">|</b><span>2019年第4期</span><b style="margin: 0 2px;">|</b><span>共12页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Zhuansun Shaoshuai&option=202" target="_blank" rel="nofollow">Zhuansun Shaoshuai;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Yang Jun‐an&option=202" target="_blank" rel="nofollow">Yang Jun‐an;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Liu Hui&option=202" target="_blank" rel="nofollow">Liu Hui;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Electronic Countermeasure InstituteNational University of Defense TechnologyHefei China;</p> <p>Electronic Countermeasure InstituteNational University of Defense TechnologyHefei China;</p> <p>Electronic Countermeasure InstituteNational University of Defense TechnologyHefei China;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/1147.html" title="应用数学">应用数学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=cognitive jamming&option=203" rel="nofollow">cognitive jamming;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=inverse reinforcement learning&option=203" rel="nofollow">inverse reinforcement learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=jamming strategy&option=203" rel="nofollow">jamming strategy;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Markov decision process&option=203" rel="nofollow">Markov decision process;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=reinforcement learning&option=203" rel="nofollow">reinforcement learning;</a> </p> <div class="translation"> 机译：认知干扰;反增强学习;干扰策略;马尔可夫决策过程;加强学习; </div> </li> </ul> </div> </div> <div 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