CAREER: Learning Agents in Dynamic, Collaborative, and Adversarial Multiagent Environments

该项目旨在使多个智能代理能够学习在实时、嘈杂、协作和对抗性的环境中针对个人和/或共同目标单独和相互协调地行动。所采取的方法将是研究特定、复杂环境中的完整代理，目标是从具体实现中吸取一般经验教训。基础研究将在四个主要领域进行。首先，多智能体强化学习将被放大，以处理比以前可能的更大和更复杂的问题。其次，将提出适合学习的新的状态表征并进行测试。第三，将研究通过预测其他代理的反应来提高代理性能的博弈论方法。第四，将开发和测试自主竞标代理的学习策略。应用领域将包括：机器人足球，无论是在模拟中还是与真实机器人一起；以及多个现实场景中的自主竞标代理。丰富的模拟环境将用于本研究，是教授学生完整的智能代理的理想基础，包括感知、认知和动作。该项目的教育目标包括利用这些领域对学生的吸引力，使其成为具有挑战性、激动人心和有指导意义的本科生和研究生课程。

This project aims to enable multiple intelligent agents to learn to act both individually and in coordination with one another towards individual and/or common goals in real-time, noisy, collaborative and adversarial environments. The approach taken will be to study complete agents in specific, complex environments, with the goal of drawing general lessons from the specific implementations. Fundamental research will be conducted in four main areas. First, multiagent reinforcement learning will be scaled up to handle larger and more complex problems than has been previously possible. Second, new state representations suitable for learning will be proposed andtested. Third, game theoretic approaches to improving agent performance by predicting the responses of other agents will be investigated. Fourth, strategies for learning autonomous biddingagents will be developed and tested. Application domains will include: robotic soccer, both in simulation and with real robots; and autonomous bidding agents in multiple realistic scenarios.The rich simulation environments to be used for this research are ideal substrates for teaching students about complete intelligent agents, including perception, cognition, and action. The educational goals of this project include leveraging the appeal of these domains to students into challenging, exciting, and instructive undergraduate and graduate courses.