Distributed Autonomous Separation Assurance with Deep Multi-Agent Reinforcement Learning

讲座名称：Distributed Autonomous Separation Assurance with Deep Multi-Agent Reinforcement Learning

讲座人：卫鹏 助理教授

讲座时间：7月2日10:00

讲座地点：腾讯会议直播 (会议ID：611 247 716 链接：https://meeting.tencent.com/s/73UIbiJrYWzU ）

讲座人介绍：

卫鹏，2007年获得清华大学学士学位，2013年获得美国普渡大学博士学位。目前为乔治华盛顿大学机械与航空航天工程系助理教授，乔治华盛顿大学的智能航空航天系统实验室 (IASL)负责人。研究领域为控制、优化、机器学习和人工智能的方面的综合应用，主要工作为航空、航空和空中机器人开发了提供了自主和决策支持工具。他目前的研究重点是复杂和不确定的动态环境中系统决策的安全性、有效性效率和可扩展性。此外为AIAA Journal of Aerospace Information Systems 的副主编。

讲座内容：

A novel deep multi-agent reinforcement learning framework is proposed to resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic sector. Currently the sector capacity is constrained by human air traffic controller's cognitive limitation. We investigate the feasibility of a new concept (autonomous separation assurance) and a new approach to push the sector capacity above human cognitive limitation. We propose the concept of using distributed vehicle autonomy to ensure separation, instead of a centralized sector air traffic controller. Our proposed framework utilizes Proximal Policy Optimization (PPO) that we modify to incorporate an attention network. This allows the agents to have access to variable aircraft information in the sector in a scalable, efficient approach to achieve high traffic throughput under uncertainty. Agents are trained using a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents. The proposed framework is validated on three challenging case studies in the BlueSky air traffic control environment. Numerical results show the proposed framework significantly reduces offline training time, increases performance, and results in a more efficient policy.

主办单位：机电工程学院