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Deep reinforcement learning for multiagent systems: a review of challenges, solutions, and applications
journal contribution
posted on 2020-09-01, 00:00 authored by Thanh Thi NguyenThanh Thi Nguyen, Ngoc Duy Nguyen, Saeid NahavandiReinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms, however, have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This article addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multiagent deep RL (MADRL) is presented, including nonstationarity, partial observability, continuous state and action spaces, multiagent training schemes, and multiagent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to the future development of more robust and highly useful multiagent learning methods for solving real-world problems.
History
Journal
IEEE transactions on cyberneticsVolume
50Issue
9Pagination
3826 - 3839Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
2168-2267Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Keywords
Continuous action spaceDeep learningDynamic programmingGamesMathematical modelObservabilityReinforcement learningRobotsScience & TechnologyTechnologyAutomation & Control SystemsComputer Science, Artificial IntelligenceComputer Science, CyberneticsComputer Sciencedeep reinforcement learning (RL)multiagentnonstationarypartial observabilityreviewroboticssurveyDYNAMICSArtificial Intelligence and Image Processing
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