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DynamicAlgorithm Selection
+addresses+the+challenge+of+dynamically+setting+hyperparameters+of+an+algorithm+for+a+diverse+set+of+instances+rather+than+focusing+solely+on+individual+tasks.+Agents+trained+with+Deep+Reinforcement+Learning+(RL)+offer+a+pathway+to+solve+such+settings.+However%252C+the+limited+generalization+performance+of+these+agents+has+significantly+hindered+the+application+in+DAC.+Our+hypothesis+is+that+a+potential+bias+in+the+training+instances+limits+generalization+capabilities.+We+take+a+step+towards+mitigating+this+by+selecting+a+representative+subset+of+training+instances+to+overcome+overrepresentation+and+then+retraining+the+agent+on+this+subset+to+improve+its+generalization+performance.+For+constructing+the+meta-features+for+the+subset+selection%252C+we+particularly+account+for+the+dynamic+nature+of+the+RL+agent+by+computing+time+series+features+on+trajectories+of+actions+and+rewards+generated+by+the+agent%27s+interaction+with+the+environment.+Through+empirical+evaluations+on+the+Sigmoid+and+CMA-ES+benchmarks+from+the+standard+benchmark+library+for+DAC%252C+called+DACBench%252C+we+discuss+the+potentials+of+our+selection+technique+compared+to+training+on+the+entire+instance+set.+Our+results+highlight+the+efficacy+of+instance+selection+in+refining+DAC+policies+for+diverse+instance+spaces.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.bc46e7d7886794c0c764f4a2a07e4d3d%3frik%3dvItz21ZCL4z9eQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Dynamic+Algorithm+Selection&FORM=IRPRST&ck=CB203B73E589ADEDE9F446E5D994EFA4&selectedIndex=13&itb=0)
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