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カーリングの局面評価関数を学習 WITH “TENSOR FLOW”
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TensorFlowのチュートリアルコードをいじってカーリングの得点予測をしました。
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カーリングの局面評価関数を学習 WITH “TENSOR FLOW”
1.
カーリングの局面評価関数を学習 WITH “TENSOR FLOW” KATSUKI
OHTO 2016/2/10
2.
自己紹介 大渡 勝己 (Katsuki
Ohto) 今は学生です 分野…ゲームAI
3.
LT内容 TensorFlow使ったことないけど 軽い気持ちでLT立候補したら いつの間にか人数増えてて 会場Googleで 穴があったら入りたい
4.
カーリング 氷上で石を投げ合って得点を競うゲーム オリンピックの時には全国的に騒がれる 複雑な要素を排除すると、 ゲーム的には 二人零和完全情報不確定ゲーム
5.
カーリングのルール 円の中心に近い方が得点をもらえる ← 黄色が3点
6.
カーリングAI 電気通信大学の研究室が 設計した戦略シミュレータ 「デジタルカーリング」 http://minerva.cs.uec.ac.jp/c urling/wiki.cgi を利用
7.
評価関数 局面の良し悪しを点数化 例)私の評価関数 connpassを見たらいつの間にか人数相当増えていた → −300 実際話している今 →
+500 (緊張で気分が高揚)
8.
カーリング評価関数 カーリングの各エンド (テニスにおけるセットのようなもの)の得点 −8 ~ +8
点 これを繰り返す →局面の情報から、−8 ~ +8のうち どの得点になるかを予測できれば良い!
9.
カーリング+TENSORFLOW INPUT 局面情報(石の配置) OUTPUT 予測される得点(の確率分布) 石の配置は画像として扱えるので、 TensorFlowのチュートリアルコード(MNIST)を (ほぼ)そのまま使ってみよう!
10.
局面の画像化 先後の石の情報を強引に1枚の白黒にする 𝑝 画素 𝑠#
盤上にある石α, 𝛽係数 𝑑(𝑝, 𝑠#) = 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑝, 𝑠#) 𝑐𝑜𝑙𝑜𝑟5 = ∑ α𝑒789 5,:; < :;(先) − ∑ α𝑒789(5,:;)< :;(後) 石の影響をガウシアン(正規分布)で加算
11.
線形関数による予測 線形関数による近似 𝑦 = 𝑊𝑥
+ 𝑏 y = tf.nn.softmax(tf.matmul(x_placeholder, W) + b) 得点予測率 41%(ランダムだと6%程度)
12.
出力クラス数 W = tf.Variable(tf.zeros([784,
17])) # 行列 b = tf.Variable(tf.zeros([17])) # ベクトル MNISTでは出力クラス数10だったところを 変えるだけでOK
13.
突っ込み そもそも棋譜のうち、 「後手1点」の割合が41% <得点の分布(x100)> ←後手 [ 0
0 2 8 32 97 239 415 15 139 32 9 3 1 0 0 0 ]先手→ 𝑦 = 𝑊𝑥 + 𝑏 バイアス項が発散しているだけ…
14.
CNNによる予測 画像認識に強いCNN (Convolutional Neural Network)ならば もっともまともな特徴抽出ができるのでは
15.
CNNによる予測 画像認識に強いCNN (Convolutional Neural Network)ならば もっともまともな特徴抽出ができるのでは …
(チュートリアルをコピペ中) 得点予測率 53%!!
16.
突っ込み(その2) 実はトレーニングデータとテストデータに 同じエンドの別局面が入っている 石が 1個増えた
17.
突っ込み(その2) ・連続値が完全一致することはほとんどないので、 完全一致した場合は得点も同じ →石の位置を丸暗記しているだけかも
18.
評価関数の出来 隠れた石を叩こうとしたらガードに引っかかった→相手有利に [0, 0, 0,
2, 2, 4, 13, 58, 1, 13, 1, 2, 0, 0, 0, 0, 0] [0, 0, 0, 0, 2, 9, 34, 35, 7, 7, 1, 1, 0, 0, 0, 0, 0]
19.
突っ込み(その3) そもそもCNNは平行移動に強いのが持ち味 カーリングの石の配置は平行移動可能なのか… 部分的には可能かもしれない 扱う対象をよく考えた方が良い
20.
感想 ・TensorFlowチュートリアルの内容だけでも すぐに応用できるので、入門としては豪華 ・モデル保存が簡単にできる。 特に複数の予測器をつなげても楽々 (先手と後手で分けたり) ・いろいろ罠にはまらないよう注意…
21.
モデル保存 運命の出会いは突然にやってくる saver = tf.train.Saver()
# 保存(読込)準備 saver.save(sess, “model.ckpt”) #保存 saver.restore(sess, “model.ckpt”)) #読込
22.
感想 ・カーリングはとりあえず機械学習を試すに はちょうどいい! ・機械学習の標準問題として カーリングを!
23.
今後のゲームAI大会 2016/3/9 Game AI
Tournament@電通大 人狼 カーリング …etc 2016/3/19 – 20 コンピュータ囲碁大会 2016/5/3 – 5 コンピュータ将棋選手権 他 ぷよぷよAI など
Editor's Notes
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