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+framework+and+introduces+new+representations+to+frame+the+problem+of+graph+data+generation+as+a+Markov+decision+process.+We+compare+the+performance+of+our+method+with+the+original+PCGRL%252C+the+run+time+with+a+random+search+and+evolutionary+algorithm%252C+and+evaluate+G-PCGRL+on+two+graph+data+domains+in+games%3a+game+economies+and+skill+trees.+The+results+show+that+our+method+is+capable+of+generating+graph-based+content+quickly+and+reliably+to+support+and+inspire+designers+in+the+game+creation+process.+In+addition%252C+trained+models+are+controllable+in+terms+of+the+type+and+number+of+nodes+to+be+generated.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.48a2e5111dad230e8d699ec94a635b1f%3frik%3dRlTeOSeGaAOynw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Procedural+Generation+Graph&FORM=IRPRST&ck=B18FFFFDF40AB9203E9B69B63174C50A&selectedIndex=1&itb=0)
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