Implement multithreading for NTBEA (ParameterSearch) and RoundRobinTournament (RunGames)

json/players/gameSpecific/TicTacToe.json

@@ -2,11 +2,11 @@
   "budgetType": "BUDGET_TIME",
   "rolloutLength": 30,
   "opponentTreePolicy": "OneTree",
-  "MASTGamma": 0,
+  "MASTGamma": 0.0,
   "heuristic": {
     "class": "players.heuristics.WinOnlyHeuristic"
   },
-  "K": 1,
+  "K": 1.0,
   "exploreEpsilon": 0.1,
   "treePolicy": "UCB",
   "MAST": "Both",

src/main/java/evaluation/RunArg.java

@@ -128,6 +128,11 @@ public enum RunArg {
     nPlayers("The number of players in each game. Overrides playerRange.",
             -1,
             new Usage[]{Usage.ParameterSearch, Usage.RunGames}),
+    nThreads("The number of threads that can be spawned in order to evaluate games.\n" +
+             "\t For tournaments (including tournaments performed after ParameterSearch), the individual matachup evaluations are parallelized;" +
+             "\t For ParameterSearch itself, the repeats are parallelized; for this part, fewer threads than specified may be allocated.",
+            1,
+            new Usage[]{Usage.ParameterSearch, Usage.RunGames}),
     neighbourhood("The size of neighbourhood to look at in NTBEA. Default is min(50, |searchSpace|/100) ",
             50,
             new Usage[]{Usage.ParameterSearch}),

src/main/java/evaluation/listeners/ActionFeatureListener.java

@@ -51,7 +51,7 @@ public String[] names() {
 
 
     @Override
-    public double[] extractFeatureVector(AbstractAction action, AbstractGameState state, int perspectivePlayer) {
+    protected double[] extractFeatureVector(AbstractAction action, AbstractGameState state, int perspectivePlayer) {
         // We put phi in first, and then psi
         double[] retValue = new double[psiFn.names().length + phiFn.names().length];
         double[] phi = cachedPhi == null ?
@@ -63,13 +63,13 @@ public double[] extractFeatureVector(AbstractAction action, AbstractGameState st
         return retValue;
     }
 
-    protected void processStateWithTargets(AbstractGameState state, AbstractAction action, Map<String, Map<AbstractAction, Double>> targets) {
+    protected synchronized void processStateWithTargets(AbstractGameState state, AbstractAction action, Map<String, Map<AbstractAction, Double>> targets) {
         actionValues = targets;
         processState(state, action);
     }
 
     @Override
-    public void processState(AbstractGameState state, AbstractAction action) {
+    public synchronized void processState(AbstractGameState state, AbstractAction action) {
         // we override this from FeatureListener, because we want to record the feature vector for each action
         if (action == null) return; // we do not record data for the GAME_OVER event
         cachedPhi = null;
@@ -109,7 +109,7 @@ private Map<String, Double> getActionScores(AbstractAction action) {
 
 
     @Override
-    public String injectAgentAttributes(String raw) {
+    public synchronized String injectAgentAttributes(String raw) {
         return raw.replaceAll(Pattern.quote("*PSI*"), psiFn.getClass().getCanonicalName())
                 .replaceAll(Pattern.quote("*PHI*"), phiFn != null ? phiFn.getClass().getCanonicalName() : "NONE");
     }

src/main/java/evaluation/listeners/FeatureListener.java

@@ -38,7 +38,7 @@ public void setLogger(IStatisticLogger logger) {
     }
 
     @Override
-    public void onEvent(Event event) {
+    public synchronized void onEvent(Event event) {
 
         if (event.type == frequency && frequency != Event.GameEvent.GAME_OVER) {
             // if GAME_OVER, then we cover this a few lines down
@@ -55,7 +55,7 @@ public void onEvent(Event event) {
     }
 
     @Override
-    public boolean setOutputDirectory(String... nestedDirectories) {
+    public synchronized boolean setOutputDirectory(String... nestedDirectories) {
 
         if (logger instanceof FileStatsLogger fileLogger) {
             fileLogger.setOutPutDirectory(nestedDirectories);
@@ -112,18 +112,18 @@ public void report() {
     }
 
     @Override
-    public void setGame(Game game) {
+    public synchronized void setGame(Game game) {
         this.game = game;
     }
 
     @Override
-    public Game getGame() {
+    public synchronized Game getGame() {
         return game;
     }
 
     public abstract String[] names();
 
-    public abstract double[] extractFeatureVector(AbstractAction action, AbstractGameState state, int perspectivePlayer);
+    protected abstract double[] extractFeatureVector(AbstractAction action, AbstractGameState state, int perspectivePlayer);
 
 
     /**

src/main/java/evaluation/listeners/MetricsGameListener.java

@@ -75,7 +75,7 @@ public MetricsGameListener(IDataLogger.ReportDestination logTo, IDataLogger.Repo
      * @param event Event has information about its type and data fields for game, state, action and player.
      *              It's not guaranteed that the data fields are different to null, so a check is necessary.
      */
-    public void onEvent(Event event) {
+    public synchronized void onEvent(Event event) {
         if (!eventsOfInterest.contains(event.type))
             return;
 
@@ -94,7 +94,7 @@ public void onEvent(Event event) {
     }
 
     @Override
-    public boolean setOutputDirectory(String... nestedDirectories) {
+    public synchronized boolean setOutputDirectory(String... nestedDirectories) {
 
         boolean success = true;
 
@@ -165,22 +165,22 @@ private String eventToIndexingColumn(IGameEvent e) {
     }
 
     /* Getters, setters */
-    public final void setGame(Game game) {
+    public synchronized final void setGame(Game game) {
         this.game = game;
     }
 
-    public final Game getGame() {
+    public synchronized final Game getGame() {
         return game;
     }
 
-    public void reset() {
+    public synchronized void reset() {
         for (AbstractMetric metric : metrics.values()) {
             metric.reset();
         }
     }
 
     @Override
-    public void init(Game game, int nPlayersPerGame, Set<String> playerNames) {
+    public synchronized void init(Game game, int nPlayersPerGame, Set<String> playerNames) {
         this.game = game;
 
         for (AbstractMetric metric : metrics.values()) {

src/main/java/evaluation/listeners/StateFeatureListener.java

@@ -30,12 +30,12 @@ public String[] names() {
     }
 
     @Override
-    public double[] extractFeatureVector(AbstractAction action, AbstractGameState state, int perspectivePlayer) {
+    protected double[] extractFeatureVector(AbstractAction action, AbstractGameState state, int perspectivePlayer) {
         return phiFn.featureVector(state, perspectivePlayer);
     }
 
     @Override
-    public String injectAgentAttributes(String raw) {
+    public synchronized String injectAgentAttributes(String raw) {
         return raw.replaceAll(Pattern.quote("*PHI*"), phiFn.getClass().getCanonicalName());
     }
 }

src/main/java/evaluation/optimisation/GameEvaluator.java

@@ -5,7 +5,6 @@
 import core.Game;
 import core.interfaces.IGameHeuristic;
 import core.interfaces.IStateHeuristic;
-import core.interfaces.IStatisticLogger;
 import evaluation.listeners.IGameListener;
 import evodef.SearchSpace;
 import evodef.SolutionEvaluator;
@@ -95,9 +94,17 @@ public double evaluate(double[] doubles) {
      */
     @Override
     public double evaluate(int[] settings) {
-        if (debug)
-            System.out.printf("Starting evaluation %d of %s at %tT%n", nEvals,
-                    Arrays.toString(settings), System.currentTimeMillis());
+        if (debug) {
+            HashMap<String, Object> chosenConfigs = new HashMap<>();
+            for (int i = 0; i < searchSpace.nDims(); i++) {
+                int finalI = i;
+                chosenConfigs.put(searchSpace.name(i), IntStream.range(0, searchSpace.nValues(i))
+                        .mapToObj(j -> searchSpace.value(finalI, j))
+                        .toList().get(settings[i]));
+            }
+            System.out.printf("%d Starting evaluation %d of %s at %tT%n", this.hashCode(), nEvals,
+                    chosenConfigs, System.currentTimeMillis());
+        }
         Object configuredThing = searchSpace.getAgent(settings);
         boolean tuningPlayer = configuredThing instanceof AbstractPlayer;
         boolean tuningGame = configuredThing instanceof Game;

src/main/java/evaluation/optimisation/MultiNTBEA.java

@@ -132,4 +132,8 @@ private static int manhattan(int[] x, int[] y) {
         return retValue;
     }
 
+    @Override
+    public NTBEA copy() {
+        return new MultiNTBEA(params, game, nPlayers);
+    }
 }