论文标题
在多机构环境中开发，评估和扩展学习代理
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
论文作者
论文摘要
DeepMind的游戏理论与多代理团队研究多学科学习的几个方面，从计算近似值到游戏理论中的基本概念，再到在富裕的空间环境中模拟社会困境，并在困难的团队协调任务中培训3-D人体机体。我们小组的一个签名目的是使用DeepMind提供的深入强化学习中提供的资源和专业知识来探索复杂环境中的多代理系统，并使用这些基准测试来促进我们的理解。在这里，我们总结了我们团队的最新工作，并提出了一种分类法，我们认为这重点介绍了多机构研究中许多重要的开放挑战。
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks. A signature aim of our group is to use the resources and expertise made available to us at DeepMind in deep reinforcement learning to explore multi-agent systems in complex environments and use these benchmarks to advance our understanding. Here, we summarise the recent work of our team and present a taxonomy that we feel highlights many important open challenges in multi-agent research.
