Geek.AI MAgent is a research platform for many-agent reinforcement learning (MARL). Unlike previous research platforms that focus on reinforcement learning research with a single agent or only few agents, MAgent aims at supporting reinforcement learning research that scales up from hundreds to millions of agents.
MAgent is able to host up to one million agents on a single GPU server. It provides environment/agent configurations and a reward description language to enable flexible environment design. Through this platform, Geek.AI has been able to demonstrate emerged collective intelligence by learning from scratch.
Geek.AI is a joint research team the UK and China, with representatives from Shanghai Jiao Tong Unviersity and University College London. They are dedicated to fundamental research topics of multi-agent reinforcement learning and its potential for real-world applications through AI coordination, learning to communicate, reinforcement learning with massive numbers of agents, self-play, learning to design multi-agent environments, personality learning for AI agents, GANs for discrete data generation, and more.
Cases for Applying Multi-Agent Reinforcement Learning -- Silo.AI
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
Lianmin Zheng, Jiacheng Yang, Han Cai, Weinan Zhang, Jun Wang, Yong Yu
Documentaries, videos and podcasts
A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
September 18, 2017
- Machine learningA field of computer science enabling computers to learn.
- Reinforcement LearningAn area of machine learning focusing on how machines and software agents react in a specific context to maximize performance and achieve reward known as reinforcement signal.
- Deep learningBranch of machine learning based on learning data representations.
- TensorFlowA machine learning software library for numerical calculation
- TensorFlow Agentsan open-source infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow, allowing new algorithms to be developed and trained efficiently
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