MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning

Population-based multi-agent reinforcement learning (PB-MARL) encompasses a range of methods that merge dynamic population selection with multi-agent reinforcement learning algorithms (MARL). While PB-MARL has demonstrated notable achievements in complex multi-agent tasks, its sequential execution is plagued by low computational efficiency due to the diversity in computing patterns and policy combinations. We propose a solution involving a stateless central task dispatcher and stateful workers to handle PB-MARL's subroutines, thereby capitalizing on parallelism across various components for efficient problem-solving. In line with this approach, we introduce MALib, a parallel framework that incorporates a task control model, independent data servers, and an abstraction of MARL training paradigms. The framework has undergone extensive testing and is available under the MIT license (https://github.com/sjtu-marl/malib) [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

Authors

• Ming Zhou Langboat Technology, Beijing, China
• Ziyu Wan
• Hanjing Wang
• Muning Wen Shanghai Jiao Tong University
• Runzhe Wu
• Ying Wen Shanghai Jiao Tong University
• Yaodong Yang Institute for Artificial Intelligence, Peking University
• Yong Yu Shanghai Jiao Tong University
• Jun Wang University College London
• Weinan Zhang Shanghai Jiao Tong University

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