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CheckersAI/src/Learn.java
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import java.util.Random; | |
public class Learn{ | |
public static void main(String[] args){ | |
final int num_games = 15; | |
LearningEvaluator le = new LearningEvaluator("../src/weights/alpha.csv", .1); | |
BaseEvaluator be = new BaseEvaluator("../src/weights/beta.csv"); | |
CheckersAI alpha = new CheckersAI(le, 1); | |
CheckersAI beta = new CheckersAI(be, 2); | |
CheckersGameState s; | |
int played = 0; | |
int won = 0; | |
int winner; | |
for(int i = 0; i < num_games; i++){ // play num_games amount of games | |
s = new CheckersGameState3(); | |
winner = play(s, alpha, beta, le); // alpha and beta play a game | |
le.updateWeights(); // get new weights using data from game | |
played++; | |
if(winner == alpha.getPlayer()){ | |
won++; | |
} | |
if(played == 10){ | |
if(won >= 7){ // if alpha wins 7 of every ten games, make beta use alpha's new evaluator | |
le.commitWeights("../src/weights/beta.csv"); | |
be.refreshWeights(); | |
} | |
played = 0; | |
won = 0; | |
} | |
} | |
} | |
// need to decide what to do if we are going on the wrong track | |
// samuel resets one of the weights to be zero | |
public static int play(CheckersGameState s, CheckersAI alpha, CheckersAI beta, LearningEvaluator le){ | |
System.out.println("playing"); | |
CheckersGameState current = s; | |
int moves = 0; // draw after 200 moves | |
Random rand = new Random(); | |
int player = rand.nextInt(2) + 1; // choose which player alpha plays as | |
int other = 1 - (player - 1) + 1; | |
System.out.println("playing as " + player); | |
alpha.setPlayer(player); | |
beta.setPlayer(other); | |
current.printState(); | |
if(other == 1){ // if beta goes first, make a move | |
current = current.result(beta.minimax(current, 7)); | |
current.printState(); | |
moves++; | |
} | |
while(!current.isTerminal() && moves <= 50){ | |
System.out.println("alphas moves:"); | |
System.out.println(current.actions()); | |
Move next = alpha.minimax(current, 7); // get alpha's move | |
le.addData(current.getFeatures(alpha.getPlayer()), next.getValue()); // add this moves data to the data set (the value of the state is stored in the move. there is probably a better way to do this) | |
current = current.result(next); // make the move | |
current.printState(); | |
moves++; | |
if(current.isTerminal()){ // if alpha won, then break | |
break; | |
} | |
current = current.result(beta.minimax(current, 7)); // beta's move | |
current.printState(); | |
moves++; | |
} | |
return current.winner(); | |
} | |
} |