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

职业：动态、协作和对抗性多智能体环境中的学习智能体

• 批准号: 0237699
• 负责人: Peter Stone
• 金额: $ 51.74万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-02-01 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0237699&HistoricalAwards=false
• 关键词: CAREER; Learning; Agents; Dynamic; Collaborative

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.

该项目旨在使多个智能代理能够学习在实时，嘈杂，协作和对抗环境中单独和相互协调地对个人和/或共同目标采取行动。 所采取的方法将是在特定的复杂环境中研究完整的代理，目的是从具体的实现中吸取一般的经验教训。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。 第二，新的状态表示适合学习将提出和测试。 第三，将研究通过预测其他代理的响应来提高代理性能的博弈论方法。 第四，学习自主biddingagents的策略将被开发和测试。 应用领域将包括：机器人足球，无论是在模拟和与真实的机器人;和自主投标代理在多个现实scenaries.The丰富的模拟环境，用于这项研究是理想的基板，教学生完整的智能代理，包括感知，认知和行动。 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，令人兴奋和指导性的本科生和研究生课程。