中国象棋chinese-chess.ppt

1、How to Win a Chinese Chess Game,Reinforcement Learning Cheng, Wen Ju,Set Up,RIVER,General,Guard,Minister,Rook,Knight,Cannon,Pawn,Training,how long does it to take for a human? how long does it to take for a computer? Chess program, “KnightCap”, used TD to learn its evaluation function while playing

2、on the Free Internet Chess Server (FICS, ), improved from a 1650 rating to a 2100 rating (the level of US Master, world champion are rating around 2900) in just 308 games and 3 days of play.,Training,to play a series of games in a self-play learning mode using temporal difference learning The goal i

3、s to learn some simple strategies piece values or weights,Why Temporal Difference Learning,the average branching factor for the game tree is usually around 30 the average game lasts around 100 ply the size of a game tree is 30100,Searching,alpha-beta search 3 ply search vs 4 ply search horizon effec

4、t quiescence cutoff search,Horizon Effect,t,t+1,t+2,t+3,Evaluation Function,feature property of the game feature evaluators Rook, Knight, Cannon , Minister, Guard, and Pawn weight: the value of a specific piece type feature function: f return the current players piece advantage on a scale from -1 to

5、 1 evaluation function: Y Y = k=1 to 7 wk * fk,TD() and Updating the Weights,wi, t+1 = wi, t + a (Yt+1 Yt)S k=1 to t l t-k wiYk = wi, t + a (Yt+1 Yt)(fi, t + l fi, t-1 + l 2fi, t-2 + + l t-1fi, 1) = 0.01 learning rate how quickly the weights can change = 0.01 feedback coefficient -how much to discou

6、nt past values,Features Table,Array of Weights,Example,t=5,t=6,t=7,t-8,Final Reward,loser if is a draw, the final reward is 0 if the board evaluation is negative, then the final reward is twice the board if the board evaluation is positive, then the final reward is -2 times the board evaluation winn

7、er if is a draw, the final reward is 0 if the board evaluation is negative, then the final reward is -2 times the board evaluation if the board evaluation is positive, then the final reward is twice the board evaluation,Final Reward,the weights are normalized by dividing by the greatest weight any n

8、egative weights are set to zero the most valuable piece has weight 1,Summary of Main Events,Reds turn Update weights for Red using TD() Red does alpha-beta search. Red executes the best move found Blues turn Update weights for Blue using TD() Blue does alpha-beta search Blue executes the best move f

9、ound (go to 1),After the Game Ends,Calculate and assign final reward for losing player Calculate and assign final reward for winning player Normalize the weights between 0 and 1,Results,10 games series 100 games series learned weights are carried over into the next series began with all weights init

10、ialized to 1 The goal is to learn the different the piece values that is close to the default values defined by H.T. Lau or even better,Observed Behavior,the early stages played pretty randomly after 20 games had identified the most valuable piece Rook after 250 games played better protecting the va

11、luable pieces, and trying to capture a valuable piece,Weights,Testing,self-play games Red played using the learned weights after 250 games Blue used H.T. Laus equivalent of the weights 5 games red won 3 blue won once one draw,Future Works,8 different types or categories of features: Piece Values Com

12、parative Piece Advantage Mobility Board Position Piece Proximity Time Value of Pieces Piece Combinations Piece Configurations,Examples,Cannon behind Knight,Conclusion,Computer Chinese chess has been studied for more than twenty years. Recently, due to the advancement of AI researches and enhancement

13、 of computer hardware in both efficiency and capacity, some Chinese chess programs with grand-master level (about 6-dan in Taiwan) have been successfully developed. Professor Shun-Chin Hsu of Chang-Jung University (CJU), who has involved in the development of computer Chinese chess programs for a lo

14、ng time of period, points out that “the strength of Chinese chess programs increase 1-dan every three years.” He also predicts that a computer program will beat the “world champion of Chinese chess” before 2012.,When and What,2004 World Computer Chinese Chess Championship Competition Dates : June 25-26, 2004 Prizes : (1) First Place USD 1,500 A gold medal (2) Second Place USD 900 A silver medal (3) Third Place USD 600 A bronze medal (4) Fourth Place USD 300,References,C. Sze