Java tutorial
/** * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. */ package org.cspoker.ai.bots.bot.gametree.mcts.nodes; import java.util.List; import java.util.Random; import org.cspoker.ai.bots.bot.gametree.action.DefaultWinnerException; import org.cspoker.ai.bots.bot.gametree.action.GameEndedException; import org.cspoker.ai.bots.bot.gametree.action.ProbabilityAction; import org.cspoker.ai.bots.bot.gametree.action.SearchBotAction; import org.cspoker.ai.bots.bot.gametree.mcts.strategies.backpropagation.BackPropagationStrategy; import org.cspoker.ai.bots.bot.gametree.mcts.strategies.selection.SelectionStrategy; import org.cspoker.ai.bots.bot.gametree.search.expander.Expander; import org.cspoker.client.common.gamestate.GameState; import org.cspoker.common.elements.player.PlayerId; import com.google.common.collect.ImmutableList; public abstract class InnerNode extends AbstractNode { private final static Random random = new Random(); //config protected final Config config; //parent public final GameState gameState; public final PlayerId bot; //children private double[] probabilities = null; private double[] cumulativeProbability = null; private ImmutableList<INode> children = null; //protected boolean inTree = false; protected final BackPropagationStrategy backPropStrategy; public InnerNode(InnerNode parent, ProbabilityAction probAction, GameState gameState, PlayerId bot, Config config) { super(parent, probAction); this.bot = bot; this.gameState = gameState; this.config = config; this.backPropStrategy = createBackPropStrategy(); } protected abstract BackPropagationStrategy createBackPropStrategy(); public INode selectRecursively() { //if(!inTree) return this; boolean needsChildExpansion = (children == null); if (needsChildExpansion) { config.getModel().assumeTemporarily(gameState); expandChildren(); } INode selectedChild = selectChild().selectRecursively(); if (needsChildExpansion) config.getModel().forgetLastAssumption(); return selectedChild; } public abstract INode selectChild(); public INode selectChild(SelectionStrategy selectionStrategy) { return selectionStrategy.select(this); } @Override public void expand() { //inTree = true; } public double simulate() { throw new IllegalStateException("Selected node must ne leaf."); } public INode getRandomChild() { double randomNumber = random.nextDouble(); ImmutableList<INode> children = getChildren(); for (int i = 0; i < cumulativeProbability.length - 1; i++) { if (randomNumber < cumulativeProbability[i]) { return children.get(i); } } return children.get(cumulativeProbability.length - 1); } public void backPropagate(double value) { backPropStrategy.onBackPropagate(value); parent.backPropagate(value); } @Override public double getEV() { return backPropStrategy.getEV(); } @Override public int getNbSamples() { return backPropStrategy.getNbSamples(); } @Override public double getStdDev() { return backPropStrategy.getStdDev(); } @Override public double getEVVar() { return backPropStrategy.getEVVar(); } @Override public double getEVStdDev() { return backPropStrategy.getEVStdDev(); } @Override public double getVariance() { return backPropStrategy.getVariance(); } @Override public int getNbSamplesInMean() { return backPropStrategy.getNbSamplesInMean(); } public ImmutableList<INode> getChildren() { return children; } @Override public GameState getGameState() { return gameState; } public Config getConfig() { return config; } protected void expandChildren() { if (children == null) { Expander expander = new Expander(gameState, config.getModel(), gameState.getNextToAct(), bot, config.getSampler()); List<ProbabilityAction> actions = expander.getProbabilityActions(); ImmutableList.Builder<INode> childrenBuilder = ImmutableList.builder(); probabilities = new double[actions.size()]; cumulativeProbability = new double[actions.size()]; double cumul = 0; for (int i = 0; i < actions.size(); i++) { ProbabilityAction action = actions.get(i); double probability = action.getProbability(); childrenBuilder.add(getChildAfter(action)); cumul += probability; cumulativeProbability[i] = cumul; probabilities[i] = probability; } children = childrenBuilder.build(); } } public INode getChildAfter(ProbabilityAction probAction) { SearchBotAction action = probAction.getAction(); if (action.endsInvolvementOf(bot)) { // bot folded return new ConstantLeafNode(this, probAction, gameState.getPlayer(bot).getStack()); } else { try { GameState nextState = action.getStateAfterAction(); // expand further if (nextState.getNextToAct().equals(bot)) { return new DecisionNode(this, probAction, nextState, bot, config); } else { return new OpponentNode(this, probAction, nextState, bot, config); } } catch (GameEndedException e) { // no active players left // go to showdown return config.getShowdownNodeFactory().create(e.lastState, this, probAction); } catch (DefaultWinnerException e) { assert e.winner.getPlayerId().equals(bot) : "Bot should have folded earlier, winner can't be " + e.winner; // bot wins return new ConstantLeafNode(this, probAction, gameState.getPlayer(bot).getStack() + (int) (e.foldState.getGamePotSize() * (1 - gameState.getTableConfiguration().getRake()))); } } } public double[] getCumulativeProbability() { return cumulativeProbability; } public double[] getProbabilities() { return probabilities; } }