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Critic inLearning Agent
+operating+in+a+partially+observable+environment.+We+assume+that+the+network+of+agents+operates+in+a+fully-decentralized+manner%252C+possessing+the+capability+to+exchange+variables+with+their+immediate+neighbors.+The+proposed+design+methodology+is+supported+by+an+analysis+demonstrating+that+the+difference+between+final+outcomes%252C+obtained+when+the+global+state+is+fully+observed+versus+estimated+through+the+social+learning+method%252C+is+%2524varepsilon%2524-bounded+when+an+appropriate+number+of+iterations+of+social+learning+updates+are+implemented.+Unlike+many+existing+dec-POMDP-based+RL+approaches%252C+the+proposed+algorithm+is+suitable+for+model-free+multi-agent+reinforcement+learning+as+it+does+not+require+knowledge+of+a+transition+model.+Furthermore%252C+experimental+results+illustrate+the+efficacy+of+the+algorithm+and+demonstrate+its+superiority+over+the+current+state-of-the-art+methods.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.317b326e515c8bb09201f539d7d5ea82%3frik%3dMPSsZXs%252bwyS%252fVQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Critic+in+Learning+Agent&FORM=IRPRST&ck=D569E368B721C87FEC7AED34BF0E09A9&selectedIndex=24&itb=0)
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