Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

• Published in: IEEE transactions on cybernetics Vol. 50; no. 9; pp. 3826 - 3839
• Main Authors: Nguyen, Thanh Thi, Nguyen, Ngoc Duy, Nahavandi, Saeid
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Continuous action space; Decision making; Deep learning; deep reinforcement learning (RL); Dynamic programming; Games; Machine learning; Mathematical model; multiagent; Multiagent systems; nonstationary; Observability; partial observability; Reinforcement learning; review; robotics; Robots; survey; Task complexity

Reinforcement 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 method...