Reinforcement learning on graphs: A survey recent years, and there has been some pioneering work employing the research-rich Reinforcement Learning (RL) techniques to address graph data mining tasks... N Mingshuo,C Dongming,W Dongqi 被引量: 0发表: 2022年 Deep reinforcement learning in computer...
今天分享的是一篇ICLR的投稿,核心是构造Graph based world model提高learning sample efficiency。 针对稀疏reward或者episode length比较长的困难任务,强化学习算法直接在原始的状态动作空间中学习策略通常效率比较低。这篇文章的核心思想是:(1)利用既有的离线数据集,基于表征学习对状态空间和动作空间进行抽象和聚合,利用...
Learning in a Multi-agent Environment Benefits of MARL Challenges of MARL computational complexity Non-stationarity Coordination Performance Evaluation Simulating MARL Tasks Social Context 个人理解以下是MARL的分类:Agent Awareness,Coordination Graphs,MAS Training Schemes, Agent Awareness Coordination Graphs MAS Tr...
ASurveyofReinforcementLearningTechniques: Strategies,RecentDevelopment,andFuture Directions AmitKumarMondal NadeemJamali UniversityofSaskatchewan,Canada amit.mondal@usask.ca,jamali@cs.usask.ca Abstract.Reinforcementlearningisoneofthecorecomponentsinde- signinganartificialintelligentsystememphasizingreal-timeresponse. ...
learning (ML) algorithm. For example, we can train ML algorithm on a dataset of already solved TSP instances to decide on which node to move next for new TSP instances. A particular branch of ML that we consider in this survey is called reinforcement learning (RL) that for a given CO ...
再比如Learning to plan,就是说planning也不是制定好的方式,比如MCTS之类的,而是像policy一样去学出来的(The idea is to optimize our planner over a sequence of tasks to eventually obtain a better planning algorithm, which is a form of meta-learning)。文中举的例子是MCTSNets,Imagination-augmented agent...
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works ha
In [9], this method has been extended for topic-aware IM, that it considers attributes of users in addition to graph properties in embedding formula and a Double Q-learning algorithm has been employed. As stated in this paper, this method can also handle dynamic propagation probabilities, but...
Looking at the XRL methods, it becomes clear that post-hoc interpretability models are much more prevalent than intinsic models. This makes sense, considering the fact that RL models were developed to solve tasks without human supervision that were too dificult for un-/supervised learning and are...
DEEP learningINTERNETWORKINGThis paper provides a comprehensive survey of the integration of graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into the fundamentals of GNN, its variants, and the state-of-the...