Files
cs6601p1/test/net/woodyfolsom/msproj/policy/MinimaxTest.java
cs6601 bb5990a04f Substantial refactoring to implement correct Naive, UCT Monte Carlo tree search methods.
Removed unnecessary distinction between policy and tree search (tree search is a special kind of policy).
Calculation of all valid moves / arbitrary sets of moves is now a seperate class, as it serves a different purpose than a policy.
Introduced regression error in AlphaBeta test.
2012-08-28 10:40:37 -04:00

35 lines
1006 B
Java

package net.woodyfolsom.msproj.policy;
import static org.junit.Assert.assertEquals;
import net.woodyfolsom.msproj.GameBoard;
import net.woodyfolsom.msproj.GameConfig;
import net.woodyfolsom.msproj.GameState;
import net.woodyfolsom.msproj.policy.Policy;
import org.junit.Test;
public class MinimaxTest {
@Test
public void testGenmove() {
Policy moveGenerator = new Minimax();
GameState gameState = new GameState(5);
gameState.playStone('A', 2, GameBoard.BLACK_STONE);
gameState.playStone('B', 1, GameBoard.BLACK_STONE);
gameState.playStone('C', 2, GameBoard.BLACK_STONE);
gameState.playStone('B', 4, GameBoard.BLACK_STONE);
String move = moveGenerator.getAction(new GameConfig(), gameState, "w");
System.out.println("Generated move: " + move);
gameState.playStone("w", move);
System.out.println(gameState);
assertEquals(Policy.PASS,moveGenerator.getAction(new GameConfig(), gameState, "?"));
System.out.println(gameState);
}
}