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

该项目旨在使多个智能代理能够在实时、嘈杂、协作和对抗的环境中学习单独行动和相互协调，以实现个人和/或共同目标。所采用的方法是在特定的、复杂的环境中研究完整的代理，目的是从特定的实现中得出一般的经验教训。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扩大规模，以处理比以前更大、更复杂的问题。其次，将提出并测试适合学习的新状态表示。第三，将研究通过预测其他智能体的反应来提高智能体性能的博弈论方法。第四，开发和测试自主投标代理学习策略。应用领域将包括：机器人足球，包括模拟和真实的机器人；以及多种现实场景下的自主招标代理。本研究使用的丰富模拟环境是教授学生完整智能代理（包括感知、认知和行动）的理想基础。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、令人兴奋和启发性的本科和研究生课程。

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.