2019 IEEE International Conference on Agents (ICA)最新文献_第3页

Multi-Agent Collaborative Exploration through Graph-based Deep Reinforcement Learning 基于图的深度强化学习的多智能体协同探索

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929168

Tianze Luo, Budhitama Subagdja, Di Wang, A. Tan

{"title":"Multi-Agent Collaborative Exploration through Graph-based Deep Reinforcement Learning","authors":"Tianze Luo, Budhitama Subagdja, Di Wang, A. Tan","doi":"10.1109/AGENTS.2019.8929168","DOIUrl":"https://doi.org/10.1109/AGENTS.2019.8929168","url":null,"abstract":"Autonomous exploration by a single or multiple agents in an unknown environment leads to various applications in automation, such as cleaning, search and rescue, etc. Traditional methods normally take frontier locations and segmented regions of the environment into account to efficiently allocate target locations to different agents to visit. They may employ ad hoc solutions to allocate the task to the agents, but the allocation may not be efficient. In the literature, few studies focused on enhancing the traditional methods by applying machine learning models for agent performance improvement. In this paper, we propose a graph-based deep reinforcement learning approach to effectively perform multi-agent exploration. Specifically, we first design a hierarchical map segmentation method to transform the environment exploration problem to the graph domain, wherein each node of the graph corresponds to a segmented region in the environment and each edge indicates the distance between two nodes. Subsequently, based on the graph structure, we apply a Graph Convolutional Network (GCN) to allocate the exploration target to each agent. Our experiments show that our proposed model significantly improves the efficiency of map explorations across varying sizes of collaborative agents over the traditional methods.","PeriodicalId":235878,"journal":{"name":"2019 IEEE International Conference on Agents (ICA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128125437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Efficient and Robust Emergence of Conventions through Learning and Staying 通过学习和停留有效而稳健地出现惯例

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929165

Wei Liu, Shuyue Hu, J. Liu, Wu Chen, Siyuan Chen, Yong Yu

{"title":"Efficient and Robust Emergence of Conventions through Learning and Staying","authors":"Wei Liu, Shuyue Hu, J. Liu, Wu Chen, Siyuan Chen, Yong Yu","doi":"10.1109/AGENTS.2019.8929165","DOIUrl":"https://doi.org/10.1109/AGENTS.2019.8929165","url":null,"abstract":"In a multi-agent system (MAS), conventions serve as an effective mechanism to reduce frictions among agents and hence solve coordination problems. Convention emergence studies how agents’ behavior patterns give rise to conventions and how efficiently a convention forms. In a networked MAS, the question focuses on how conventions can arise when the agents’ positions are constrained. In this paper, we investigate convention emergence under the multi-player synchronous interaction model in networked MASs. In particular, we focus on the scenario that the agents is not informed the actions played by other agents, and the only information agents can perceive is whether an interaction is success or not. To facilitate the emergence of conventions, we propose a novel approach, namely Win-Stay-Lose-Learn (WSLL), to solve the problem of no observation and shorten the action transformation time when convention seeds conflict among agents. We conduct experiments to verify the robustness and effectiveness of our proposed method, experimental results show that our method outperforms other baseline approaches in terms of convergence speed under various circumstances.","PeriodicalId":235878,"journal":{"name":"2019 IEEE International Conference on Agents (ICA)","volume":"12 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126526742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ICA 2019 Author Index ICA 2019作者索引

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/agents.2019.8929143

引用次数: 0

ICA 2019 Cover Page ICA 2019封面

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/agents.2019.8929186

引用次数: 0

ICA 2019 Preface

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/agents.2019.8929117

引用次数: 0