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	Learning to Play Hard Exploration Games Using Graph-guided Self-navigation
	Zhao EM(赵恩民)1,2; Yan RY(闫仁业)1,2; Li K(李凯)2; Li LJ(李丽娟)2; Xing JL(兴军亮)1,2
	2021-02
会议日期	2021-02
会议地点	线上
DOI	无
英文摘要	Thisworkconsiderstheproblemofdeeprein- forcementlearning(RL)withlongtimedependenciesands- parserewards,asarefoundinmanyhardexplorationgames. Agraph-basedrepresentationisproposedtoallowanagent toperformself-navigationforenvironmentalexploration.The graphrepresentationnotonlyeffectivelymodelstheenvironment structure,butalsoefficientlytracestheagentstatechangesand thecorrespondingactions.Byencouragingtheagenttoearna newinfluence-basedcuriosityrewardfornewgameobservations, thewholeexplorationtaskisdividedintosub-tasks,whichare effectivelysolvedusingaunifieddeepRLmodel.Experimental evaluationsonhardexplorationAtariGamesdemonstratethe effectivenessoftheproposedmethod.Thesourcecodeand learnedmodelswillbereleasedtofacilitatefurtherstudieson thisproblem.
学科主题	信息科学与系统科学
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/52241]
专题	融合创新中心_决策指挥与体系智能
通讯作者	Xing JL(兴军亮)
作者单位	1.SchoolofArtificialIntelligence,UniversityofChineseAcademyofSciences 2.Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Zhao EM,Yan RY,Li K,et al. Learning to Play Hard Exploration Games Using Graph-guided Self-navigation[C]. 见:. 线上. 2021-02.

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