Collaborative Research: Mutual Learning: A Systems Theoretic Investigation

协作研究：相互学习：系统理论研究

• 批准号: 1930606
• 负责人: Snehasis Mukhopadhyay
• 金额: $ 26.86万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1930606&HistoricalAwards=false
• 关键词: Collaborative; Research; Mutual; Learning; Systems

Mutual learning can happen between two humans, a human and machine, or between two machines. The first class is of interest to researchers in the field of social psychology. The importance of human machine interactions is being felt in many situations and most recently in the interaction between the human driven and completely autonomous vehicles. The proposed research deals with machine-machine learning to investigate efficient cooperation between machines, but also reveal the limitations of this cooperation. In particular, the research will attempt to answer questions such as whether two agents, although individually using schemes that will result in the desired behavior, may arrive at wrong conclusion using mutual learning.While the term mutual learning has been used by other investigators in the past, our objective is to investigate it in a quantitative sense within the framework of mathematical systems theory. The problems proposed for investigation include deterministic optimization in high dimensional spaces, stochastic reinforcement learning in static/stationary environments (learning automata) using both deterministic and stochastic schemes, learning in dynamic environments such as the ones described by Markov Decision Processes, and learning/adaptation by multiple agents in dynamic environments described by deterministic or stochastic difference and differential equations. The results on mutual learning obtained during the proposed project will be widely disseminated at national and international conferences as well as the bi-annual Yale Workshops on Adaptive and Learning Systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

相互学习可以发生在两个人之间，一个人和机器之间，或者两台机器之间。 第一类是社会心理学领域的研究人员感兴趣的。 人机交互的重要性在许多情况下都能感受到，最近在人类驾驶和完全自动驾驶车辆之间的交互中也能感受到。拟议的研究涉及机器-机器学习，以研究机器之间的有效合作，但也揭示了这种合作的局限性。 特别是，研究将试图回答这样的问题，如两个代理，虽然单独使用的方案，将导致所需的行为，可能会得出错误的结论，使用相互学习。虽然术语相互学习已被其他研究人员在过去，我们的目标是调查在定量意义上的数学系统理论的框架内。提出的调查问题包括确定性优化在高维空间，随机强化学习在静态/静态环境（学习自动机）使用确定性和随机计划，学习在动态环境中，如马尔可夫决策过程描述的，学习/适应多个代理在动态环境中描述的确定性或随机差分和微分方程。在拟议的项目中获得的相互学习的结果将在国家和国际会议以及两年一度的耶鲁适应和学习系统研讨会上广泛传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Mutual Learning in Optimizatio

优化中的相互学习

• 发表时间: 2022
• 期刊: and Cybernetics (SMC
• 作者: Narendra, K.S.;Mukhopadhyay, S.;Esfandiari, K
• 通讯作者: Esfandiari, K

Mutual Q-Learning

相互 Q 学习

• 发表时间: 2020
• 期刊: IEEE
• 作者: Reid, C.;Mukhopadhyay, S.
• 通讯作者: Mukhopadhyay, S.

Mutual Learning: Part II --Reinforcement Learning

相互学习：第二部分——强化学习

• DOI: 10.23919/acc45564.2020.9147838
• 发表时间: 2020
• 期刊: 2020 American Control Conference (ACC
• 作者: Narendra, Kumpati S.;Mukhopadhyay, Snehasis
• 通讯作者: Mukhopadhyay, Snehasis

Mutual Learning for Pattern Recognition for Increasing Prediction Accuracy

模式识别的相互学习可提高预测精度

• 发表时间: 2023
• 期刊: and Cybernetics Conference (IEEE SMC
• 作者: Chowdhury, S.;Narendra, K. S.;Mukhopadhyay, S.
• 通讯作者: Mukhopadhyay, S.

Mutual reinforcement learning with heterogeneous agents

与异构代理的相互强化学习

• 发表时间: 2021
• 期刊: IEEE.
• 作者: Reid, C;Mukhopadhyay, S.
• 通讯作者: Mukhopadhyay, S.