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Learning Efficient Multi-Agent Cooperative Visual Exploration
Chao Yu · Jiaxuan Gao · Huazhong Yang · Yu Wang · Yi Wu

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in Deep Reinforcement Learning »
Event URL: https://openreview.net/forum?id=-4Yz4vU4uN5 »

We consider the task of visual indoor exploration with multiple agents, where the agents need to cooperatively explore the entire indoor region using as few steps as possible. Classical planning-based methods often suffer from particularly expensive computation at each inference step and a limited expressiveness of cooperation strategy. By contrast, reinforcement learning (RL) has become a trending paradigm for tackling this challenge due to its modeling capability of arbitrarily complex strategies and minimal inference overhead. We extend the state-of-the-art single-agent RL solution, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based global-goal planner, Spatial Coordination Planner (SCP), which leverages spatial information from each individual agent in an end-to-end manner and effectively guides the agents to navigate towards different spatial goals with high exploration efficiency. SCP consists of a transformer-based relation encoder to capture intra-agent interactions and a spatial action decoder to produce accurate goals. In addition, we also implement a few multi-agent enhancements to process local information from each agent for an aligned spatial representation and more precise local planning. Our final solution, Multi-Agent Active Neural SLAM (MAANS), combines all these techniques and substantially outperforms 4 different planning-based methods and various RL baselines in the photo-realistic physical testbed, Habitat.

Author Information

Chao Yu (Tsinghua University)
Jiaxuan Gao (Tsinghua University, Tsinghua University)
Huazhong Yang
Yu Wang (Tsinghua University)

Yu Wang received his B.S. degree in 2002 and Ph.D. degree (with honor) in 2007 from Tsinghua University, Beijing. He is currently a Tenured Associate Professor with the Department of Electronic Engineering, Tsinghua University. His research interests include brain inspired computing, application specific hardware computing, parallel circuit analysis, and power/reliability aware system design methodology. Dr. Wang has authored and coauthored over 150 papers in refereed journals and conferences. He has received Best Paper Award in FPGA 2017, ISVLSI 2012, and Best Poster Award in HEART 2012 with 8 Best Paper Nominations. He is a recipient of IBM X10 Faculty Award in 2010. He served as TPC chair for ICFPT 2011 and Finance Chair of ISLPED 2012-2016, and served as program committee member for leading conferences in these areas, including top EDA conferences such as DAC, DATE, ICCAD, ASP-DAC, and top FPGA conferences such as FPGA and FPT. Currently he serves as Co-EIC for SIGDA E-Newsletter, Associate Editor for IEEE Transactions on CAD and Journal of Circuits, Systems, and Computers. He also serves as guest editor for Integration, the VLSI Journal and IEEE Transactions on Multi-Scale Computing Systems. He is a recipient of NSFC Excellent Young Scholar，and is now serving as ACM distinguished speaker. He is an IEEE/ACM senior member.

Yi Wu (OpenAI)

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