Examples with SlidingMeanEDA ntuple.SlidingMeanEDA used on opensource projects

Example 6 with SlidingMeanEDA

use of ntuple.SlidingMeanEDA in project SimpleAsteroids by ljialin.

the class EvoAgentSearchSpaceAsteroids method evaluate.

@Override
public double evaluate(int[] x) {
    // create a problem to evaluate this one on ...
    // this should really be set externally, but just doing it this way for now
    AsteroidsGameState gameState = new AsteroidsGameState().setParams(params).initForwardModel();
    // search space will need to be set before use
    DefaultMutator mutator = new DefaultMutator(null);
    mutator.pointProb = pointMutationRate[x[pointMutationRateIndex]];
    mutator.flipAtLeastOneValue = flipAtLeastOneBit[x[flipAtLeastOneBitIndex]];
    // setting to true may give best performance
    mutator.totalRandomChaosMutation = false;
    SimpleRMHC simpleRMHC = new SimpleRMHC();
    simpleRMHC.setSamplingRate(nResamples[x[nResamplesIndex]]);
    simpleRMHC.setMutator(mutator);
    EvoAlg sliding = new SlidingMeanEDA();
    EvoAgent evoAgent = new EvoAgent().setEvoAlg(simpleRMHC, getNEvals(x));
    // EvoAgent evoAgent = new EvoAgent().setEvoAlg(sliding, getNEvals(x));
    evoAgent.setUseShiftBuffer(useShiftBuffer[x[useShiftBufferIndex]]);
    evoAgent.setSequenceLength(seqLength[x[seqLengthIndex]]);
    for (int i = 0; i < maxTick; i++) {
        int action = evoAgent.getAction(gameState, 0);
        gameState.next(new int[] { action });
    }
    double fitness = gameState.getScore();
    logger.log(fitness, x, false);
    return fitness;
}

Example 7 with SlidingMeanEDA

use of ntuple.SlidingMeanEDA in project SimpleAsteroids by ljialin.

the class GameRunnerTest method main.

public static void main(String[] args) {
    GameState.includeBuffersInScore = false;
    GameRunner gameRunner = new GameRunner().setLength(200);
    SimplePlayerInterface p1, p2;
    p1 = new RandomAgent();
    // p2 = new DoNothingAgent();
    EvoAlg evoAlg1 = new SimpleRMHC();
    // evoAlg1.mu
    int nEvals1 = 200;
    int seqLength1 = 10;
    int nEvals2 = 400;
    int seqLength2 = 5;
    SlidingMeanEDA evoAlg2 = new SlidingMeanEDA().setHistoryLength(40);
    SimpleGA simpleGA = new SimpleGA().setPopulationSize(20);
    EvoAgent evoAgent1 = new EvoAgent().setEvoAlg(evoAlg1, nEvals1).setSequenceLength(seqLength1);
    evoAgent1.setUseShiftBuffer(true);
    // evoAgent1.mu
    // evoAgent1.u
    // evoAgent1.setOpponent(new RandomAgent()).setUseShiftBuffer(false);
    // evoAgent1.setOpponent(new RandomAgent());
    p1 = evoAgent1;
    SimplePlayerInterface opponentModel;
    opponentModel = new DoNothingAgent();
    opponentModel = new RandomAgent();
    // p2 = new EvoAgent().setEvoAlg(simpleGA, nEvals).setSequenceLength(seqLength).setOpponent(opponentModel);
    // p2 = new EvoAgent().setEvoAlg(evoAlg1, nEvals/2).setSequenceLength(seqLength*2).setOpponent(opponentModel);
    EvoAgent evoAgent2 = new EvoAgent().setEvoAlg(evoAlg1, nEvals2).setSequenceLength(seqLength2).setOpponent(opponentModel);
    evoAgent2.setUseShiftBuffer(true);
    p2 = evoAgent2;
    p2 = getMCTSAgent(new GameState().defaultState(), 1);
    // p2 = new RandomAgent();
    gameRunner.setPlayers(p1, p2);
    // now play a number of games and observe the outcomes
    // verbose is set to true by default so after the games have been played
    // it will report the outcomes
    int nGames = 20;
    gameRunner.playGames(nGames);
    // now access the game logs to plot the scores
    gameRunner.plotGameScores();
// System.out.println(evoAlg2.pVec);
}

Example 8 with SlidingMeanEDA

use of ntuple.SlidingMeanEDA in project SimpleAsteroids by ljialin.

the class AgentEvaluator method evaluate.

@Override
public double evaluate(int[] solution) {
    // at thias point,
    System.out.println("Params are:");
    System.out.println(searchSpace.report(solution));
    // can also override parameters by setting them directly as follows:
    BattleGameParameters.loss = 0.996;
    BattleGameParameters.thrust = 3;
    // BattleGameParameters.shipSize *= 2;
    // BattleGameParameters.damageRadius *= 2;
    SpaceBattleLinkStateTwoPlayer linkState = new SpaceBattleLinkStateTwoPlayer();
    StateObservationMulti multi = linkState;
    GameActionSpaceAdapterMulti.useHeuristic = false;
    // DefaultMutator.totalRandomChaosMutation = false;
    ElapsedCpuTimer timer = new ElapsedCpuTimer();
    // AbstractMultiPlayer player2;
    int idPlayer1 = 0;
    int idPlayer2 = 1;
    // player2 = new controllers.multiPlayer.discountOLMCTS.Agent(linkState, timer, idPlayer2);
    // try the evolutionary players
    int nResamples = 2;
    EvoAlg evoAlg = new SimpleRMHC(nResamples);
    double kExplore = searchSpace.getExplorationFactor(solution);
    int nNeighbours = 100;
    int nEvals = 100;
    evoAlg = new NTupleBanditEA(kExplore, nNeighbours);
    evoAlg = new SlidingMeanEDA().setHistoryLength(searchSpace.getHistoryLength(solution));
    Agent evoAgent = new controllers.multiPlayer.ea.Agent(linkState, timer, evoAlg, idPlayer1, nEvals);
    evoAgent.setDiscountFactor(searchSpace.getDiscountFactor(solution));
    evoAgent.sequenceLength = searchSpace.getRolloutLength(solution);
    // evoAgent.di
    // EvoAlg evoAlg2 = new CompactSlidingModelGA().setHistoryLength(2);
    EvoAlg evoAlg2 = new SlidingMeanEDA().setHistoryLength(2);
    Agent player2 = new controllers.multiPlayer.ea.Agent(linkState, timer, evoAlg2, idPlayer2, nEvals);
    player2.sequenceLength = 5;
    // player2 = new controllers.multiPlayer.ea.Agent(linkState, timer, new SimpleRMHC(nResamples), idPlayer2, nEvals);
    // player1  = new controllers.multiPlayer.smlrand.Agent();
    // EvoAlg evoAlg2 = new SimpleRMHC(2);
    // player1 = new controllers.multiPlayer.ea.Agent(linkState, timer, evoAlg2, idPlayer1, nEvals);
    // in milliseconds
    int thinkingTime = 10;
    int delay = 10;
    // player = new controllers.singlePlayer.sampleRandom.Agent(stateObs, timer);
    // check that we can play the game
    Random random = new Random();
    int nSteps = 500;
    ElapsedTimer t = new ElapsedTimer();
    StatSummary sst1 = new StatSummary("Player 1 Elapsed Time");
    StatSummary sst2 = new StatSummary("Player 2 Elapsed Time");
    StatSummary ssTicks1 = new StatSummary("Player 1 nTicks");
    StatSummary ssTicks2 = new StatSummary("Player 2 nTicks");
    for (int i = 0; i < nSteps && !linkState.isGameOver(); i++) {
        linkState.state = linkState.state.copyState();
        timer = new ElapsedCpuTimer();
        timer.setMaxTimeMillis(thinkingTime);
        ElapsedTimer t1 = new ElapsedTimer();
        // keep track of the number of game ticks used by each algorithm
        int ticks;
        ticks = SpaceBattleLinkStateTwoPlayer.nTicks;
        Types.ACTIONS action1 = evoAgent.act(multi.copy(), timer);
        sst1.add(t1.elapsed());
        ticks = SpaceBattleLinkStateTwoPlayer.nTicks - ticks;
        ssTicks1.add(ticks);
        // System.out.println("Player 1 Ticks = " + ticks);
        ElapsedTimer t2 = new ElapsedTimer();
        ticks = SpaceBattleLinkStateTwoPlayer.nTicks;
        Types.ACTIONS action2 = player2.act(multi.copy(), timer);
        sst2.add(t2.elapsed());
        ticks = SpaceBattleLinkStateTwoPlayer.nTicks - ticks;
        ssTicks2.add(ticks);
        // System.out.println("Player 2 Ticks = " + ticks);
        multi.advance(new Types.ACTIONS[] { action1, action2 });
    }
    System.out.println(multi.getGameScore());
    System.out.println(multi.isGameOver());
    // System.out.println(SingleTreeNode.rollOutScores);
    System.out.println(sst1);
    System.out.println(sst2);
    System.out.println(ssTicks1);
    System.out.println(ssTicks2);
    double score = multi.getGameScore(0);
    System.out.println("Game score: " + score);
    if (score > 0)
        return 1;
    if (score < 0)
        return -1;
    return 0;
}