Re: [Computer-go] UEC cup 2nd day

FineArt won. (;GM[1]SZ[19] PB[zen] PW[fineart] DT[2017-03-19]RE[W+R]KM[6.5]TM[30]RU[Japanese]PC[UEC, Tokyo] ;B[qd];W[dc];B[pq];W[dp];B[oc];W[po];B[qo];W[qn];B[qp];W[pm] ;B[pj];W[oq];B[pp];W[op];B[oo];W[pn];B[no];W[or];B[pr];W[lq] ;B[lo];W[rn];B[kq];W[kr];B[mr];W[mq];B[lr];W[kp];B[jq];W[lp]

[Computer-go] Training a "Score Network" in Monte-Carlo Tree Search

Training a policy network is simple and I have found a Residual Network with Batch Normalization works very well. However training a value network is far more challenging as I have found it indeed very easy to have overfitting, unless one uses the final territory as another prediction target. Even

Re: [Computer-go] Training a "Score Network" in Monte-Carlo Tree Search

A few more wordsŠ *) Pushing this idea to the extreme, one might want to build a "Tree Network" whose output tries to somehow fit the whole Monte-Carlo Search Tree (including all the win/lose numbers etc.) for the board position. As we know a deep network can fit anything. The structure of the