id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
xg94hm54j30	Martin Figura	Cooperative Multi-Agent Reinforcement Learning in Decentralized Networks	2022		.txt	text/plain	371	16	34	We show that in the presence of Byzantine agents, whose estimation and communication strategies are arbitrary, the estimates of the cooperative agents converge to a bounded consensus value, provided that there are at most H Byzantine agents in the network that is (2H+1)-robust. This dissertation aims to address two challenges in decentralized cooperative multi-agent reinforcement learning, a new training paradigm that features scalability and privacy guarantees for cooperative agents.	cache/xg94hm54j30.txt	txt/xg94hm54j30.txt