Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/** Copyright by Barry G. Becker, 2000-2011. Licensed under MIT License: http://www.opensource.org/licenses/MIT */
package com.barrybecker4.game.twoplayer.common.search.strategy;
import com.barrybecker4.game.common.GameContext;
import com.barrybecker4.game.common.MoveList;
import com.barrybecker4.game.twoplayer.common.TwoPlayerBoard;
import com.barrybecker4.game.twoplayer.common.TwoPlayerMove;
import com.barrybecker4.game.twoplayer.common.WinProbabilityCalculator;
import com.barrybecker4.game.twoplayer.common.search.Searchable;
import com.barrybecker4.game.twoplayer.common.search.options.SearchOptions;
import com.barrybecker4.game.twoplayer.common.search.tree.SearchTreeNode;
import com.barrybecker4.optimization.parameter.ParameterArray;
import java.util.List;
/**
* Implementation of Upper Confidence Tree (UCT) search strategy.
* This method uses a monte carlo (stochastic) method and is fundamentally different than minimax and its derivatives.
* It's subclasses define the key search algorithms for 2 player zero sum games with perfect information.
*
* - add option to use concurrency. Need lock on uctNodes
*
* @author Barry Becker
*/
public class UctStrategy>
extends AbstractSearchStrategy {
/** ratio of exploration to exploitation (of known good moves) while searching. */
private double exploreExploitRatio;
/** Number of moves to play in a random game from the starting move state */
private int numRandomLookAhead;
/** When selecting a random move for a random game, select from only this many of the top moves. */
private int percentLessThanBestThresh;
/**
* Constructor - do not call directly.
* @param searchable the thing to be searched that has options and can make/undo moves.
* @param weights coefficients for the evaluation polynomial that indirectly determines the best move.
*/
public UctStrategy( Searchable searchable, ParameterArray weights ) {
super(searchable, weights);
exploreExploitRatio = getOptions().getMonteCarloSearchOptions().getExploreExploitRatio();
numRandomLookAhead = getOptions().getMonteCarloSearchOptions().getRandomLookAhead();
percentLessThanBestThresh = getOptions().getBestMovesSearchOptions().getPercentLessThanBestThresh();
}
@Override
public SearchOptions getOptions() {
return searchable.getSearchOptions();
}
@Override
public EvaluationPerspective getEvaluationPerspective() {
return EvaluationPerspective.ALWAYS_PLAYER1;
}
/**
* {@inheritDoc}
*
* Move UCTSearch(int numsim) {
* root = new Node(-1,-1); //init uct tree
* createChildren(root);
* Board clone=new Board();
* for (int i=0; i root = new UctNode<>(lastMove);
while (numSimulations < maxSimulations ) {
playSimulation(root, parent);
numSimulations++;
percentDone = (100 * numSimulations) / maxSimulations;
}
return root.findBestChildMove(getOptions().getMonteCarloSearchOptions().getMaxStyle());
}
/**
* This recursive method ultimately expands the in-memory game tree by one node and updates that node's parents.
*
* return 0=lose 1=win for current player to move
* int playSimulation(Node n) {
int randomresult=0;
if (n.child==null && n.visits<10) { // 10 simulations until chilren are expanded (saves memory)
randomresult = playRandomGame();
}
else {
if (n.child == null) createChildren(n);
Node next = UCTSelect(n); // select a move
if (next==null) { ERROR }
makeMove(next.x, next.y);
int res=playSimulation(next);
randomresult = 1-res;
}
n.update(1-randomresult); //update node (Node-wins are associated with moves in the Nodes)
return randomresult;
* }
* @return chance of player1 winning when running a simulation from this board position.
* This probability is a number between 0 and 1 inclusive.
*/
public float playSimulation(UctNode lastMoveNode, SearchTreeNode parent) {
float player1Score;
if (lastMoveNode.getNumVisits() == 0) {
player1Score = playRandomGame(lastMoveNode.move);
movesConsidered++;
}
else {
UctNode nextNode = null;
if (!searchable.done(lastMoveNode.move, false)) {
if (!lastMoveNode.hasChildren()) {
int added = lastMoveNode.addChildren(searchable.generateMoves(lastMoveNode.move, weights_));
if (added == 0) {
GameContext.log(0, "No moves added for " + lastMoveNode);
}
addNodesToTree(parent, lastMoveNode.getChildren());
}
nextNode = uctSelect(lastMoveNode);
}
// may be null if there are no move valid moves or lastMoveNode won the game.
if (nextNode != null) {
searchable.makeInternalMove(nextNode.move);
SearchTreeNode nextParent = (parent != null) ? parent.findChild(nextNode.move) : null;
player1Score = playSimulation(nextNode, nextParent);
searchable.undoInternalMove(nextNode.move);
} else {
player1Score = WinProbabilityCalculator.getChanceOfPlayer1Winning(lastMoveNode.move.getValue());
}
}
lastMoveNode.update(player1Score);
if (parent != null)
parent.attributes = lastMoveNode.getAttributes();
return player1Score;
}
/**
* Selects the best child of parentNode.
*
* // Larger values give uniform search
* // Smaller values give very selective search
* public Node UCTSelect(Node node) {
* Node res=null;
* Node next = node.child;
* double best_uct=0;
* while (next!=null) { // for all children
* uctvalue = next.calcUctValue(exploreExploit, numVisists)
* if (uctvalue > best_uct) { // get max uctvalue of all children
* best_uct = uctvalue;
* res = next;
* }
* next = next.sibling;
* }
* return res;
* }
* @return the best child of parentNode. May be null if there are no next moves.
*/
private UctNode uctSelect(UctNode parentNode) {
double bestUct = -1.0;
UctNode selected = null;
for (UctNode child : parentNode.getChildren()) {
double uctValue = child.calculateUctValue(exploreExploitRatio, parentNode.getNumVisits());
if (uctValue > bestUct) {
bestUct = uctValue;
selected = child;
}
}
return selected;
}
/**
* Plays a semi-random game from the current node position.
* Its semi random in the sense that we try to avoid obviously bad moves.
* @return a score (0 = p1 lost; 0.5 = tie; or 1= p1 won) indication p1 advantage.
*/
private float playRandomGame(M lastMove) {
Searchable s = searchable.copy();
return playRandomMove(lastMove, s, s.getNumMoves());
}
/**
* Plays a semi-random game from the current node position.
* Its semi-random in the sense that we try to avoid obviously bad moves.
*
*
* public void makeRandomMove() {
* int x=0;
* int y=0;
* while (true) {
* x=rand.nextInt(BOARD_SIZE);
* y=rand.nextInt(BOARD_SIZE);
* if (f[x][y]==0 && isOnBoard(x,y)) break;
* }
* makeMove(x,y);
* }
*
* // return 0=lose 1=win for current player to move
* int playRandomGame() {
* int cur_player1=cur_player;
* while (!isGameOver()) {
* makeRandomMove();
* }
* return getWinner() == curplayer1 ? 1 : 0;
* @param startNumMoves how many moves have already been played.
* @return a score (0 = p1 lost; 0.5 = tie; or 1= p1 won) indication p1 advantage.
*/
private float playRandomMove(
M lastMove, Searchable searchable, int startNumMoves) {
int numRandMoves = searchable.getNumMoves() - startNumMoves;
if (numRandMoves >= numRandomLookAhead || searchable.done(lastMove, false)) {
int score = searchable.worth(lastMove, weights_);
lastMove.setValue(score);
return WinProbabilityCalculator.getChanceOfPlayer1Winning(score);
}
MoveList moves = searchable.generateMoves(lastMove, weights_);
if (moves.size() == 0) {
return WinProbabilityCalculator.getChanceOfPlayer1Winning(lastMove.getValue());
}
M randomMove = moves.getRandomMoveForThresh(percentLessThanBestThresh);
searchable.makeInternalMove(randomMove);
return playRandomMove(randomMove, searchable, startNumMoves);
}
/**
* add a move to the visual game tree (if parent not null).
* If the new node is already in the tree, do not add it, but maybe update values.
*/
protected void addNodesToTree(SearchTreeNode parent, List> childUctNodes) {
if (parent == null) return;
for (UctNode child : childUctNodes) {
addNodeToTree(parent, child.move, child.getAttributes());
}
}
}
