未来的背景

在2017年，您和您的对手将在一场只有未来一场生存的未来派枪战中对峙。您是否有足够的经验击败对手？现在是时候以您最喜欢的编程语言来提高您的枪支技能，并与一切斗争吧！

比赛结果

本次比赛在早上UTC 2月2日结束的^第二，2017年由于我们的选手，我们有一个令人兴奋的未来的比赛！

经过与CBetaPlayer和StudiousPlayer的激烈角逐，MontePlayer是最终的赢家。三位关岛顶级决斗者拍摄了纪念照：

                MontePlayer                         - by TheNumberOne
              +------------+
  CBetaPlayer |            |                        - by George V. Williams
 +------------+    #  1    | StudiousPlayer         - by H Walters
 |                         +----------------+
 |    #  2                        #  3      |       
 +------------------------------------------+
    The Futurustic Gun Duel @ PPCG.SE 2017

恭喜获奖者！详细的排行榜可以在本文结尾处看到。

一般指导

• 访问官方资源库以获取本比赛中使用的源代码。
• C ++条目：请继承Player该类。
• 非C ++条目：在非C ++提交的接口部分中选择一个接口。
• 当前允许使用非C ++语言：Python 3，Java。

决斗

• 每个玩家都从一门可以装无限量弹药的枪支开始。
• 每回合，玩家将同时从以下动作之一中进行选择：
  • 0 -将1枚弹药装入枪中。
  • 1-向对手发射子弹; 费用为1装弹药。
  • 2-向对手发射等离子束；花费2装弹药。
  • - -使用金属屏蔽罩防御进来的子弹。
  • = -使用热偏转器保护入射的等离子体束。
• 如果两个玩家都在第100 ^个回合后幸存下来，他们都会精疲力竭至死，从而导致平局。

如果玩家失去了对决，

• 难道不使用金属屏蔽捍卫传入的子弹。
• 难道不使用热导流捍卫传入的血浆。
• 在没有装载足够弹药的情况下开枪，在这种情况下他们的枪会自行爆炸并杀死车主。

注意事项

根据《未来枪支拥有者手册》：

• 金属屏蔽CAN NOT从传入等离子体束保卫。同样，热导流板CAN NOT从传入子弹保卫。
• 等离子束压倒了子弹（因为前者需要更多的加载弹药）。因此，如果玩家向在同一回合中发射子弹的对手发射等离子束，则对手会被杀死。
• 如果两个玩家在同一回合向对方发射子弹，则子弹会抵消，并且两个玩家都可以幸存。同样，如果两个玩家都在同一回合中向对方发射等离子束，则两个玩家都将幸免。

还值得注意的是：

• 你会不知道在你的回合中对手的动作，直到结束。
• 偏转等离子束和屏蔽子弹不会伤害您的对手。

因此，每回合共有25个有效的动作组合：

+-------------+---------------------------------------------+
|   Outcome   |               P L A Y E R   B               |
|    Table    +--------+-----------------+------------------+
| for Players | Load   | Bullet   Plasma | Metal    Thermal |
+---+---------+--------+--------+--------+--------+---------+
| P | Load    |        | B wins | B wins |        |         |
| L +---------+--------+--------+--------+--------+---------+
| A | Bullet  | A wins |        | B wins |        | A wins  |
| Y |         +--------+--------+--------+--------+---------+
| E | Plasma  | A wins | A wins |        | A wins |         |
| R +---------+--------+--------+--------+--------+---------+
|   | Metal   |        |        | B wins |        |         |
|   |         +--------+--------+--------+--------+---------+
| A | Thermal |        | B wins |        |        |         |
+---+---------+--------+--------+---------------------------+

Note: Blank cells indicate that both players survive to the next turn.

决斗示例

这是我曾经和朋友打过的对决。那时，我们对编程并不了解，因此我们使用手势并以每秒两转的速度发出信号。从左到右，我们的行动依次是：

    Me: 001-000-1201101001----2
Friend: 00-10-=1-==--0100-1---1

按照上述规则，我输了。你明白为什么吗？这是因为当我只有1枚弹药时，我发射了最后的等离子束，导致我的枪爆炸。

C ++播放器

你，作为一个文明未来的程序员，也不会直接处理枪支。相反，您编写的代码Player会与他人抗争。通过在GitHub项目中公开继承c ++类，您可以开始编写城市传奇。

Player.hpp can be found in Tournament\Player.hpp
An example of a derived class can be found in Tournament\CustomPlayer.hpp

你必须或可以做什么

• 您必须Player通过公共继承来继承类，并声明您的类为final。
• 您必须重写Player::fight，Player::Action每次调用时都会返回一个有效值。
• （可选）超越Player::perceive并Player::declared留意对手的行动并跟踪您的胜利。
• （可选）在派生类中使用私有静态成员和方法来执行更复杂的计算。
• （可选）使用其他C ++标准库。

你不能做什么

• 你必须不使用任何直接的方法来识别大于给定对手标识符，它在每场比赛的开始洗牌其他对手。您只能通过锦标赛中的玩法来猜测谁是谁。
• 你一定不要覆盖任何方法Player未声明的虚拟类。
• 你一定不声明或初始化在全球范围内任何东西。
• 由于（现为不合格）登场BlackHatPlayer，玩家不要让其在偷看或修改你的对手的状态。

决斗示例

使用GunDuel类执行枪决斗的过程。有关打架的示例，请参阅“ 启动决斗”Source.cpp一节中的。

我们展示GunClubPlayer，HumanPlayer以及GunDuel类，它可以在发现Tournament\存储库的目录。

在每个决斗中，GunClubPlayer都会装一颗子弹；开火 冲洗并重复。在每个回合中，HumanPlayer都会提示您采取行动与对手对抗。你的键盘控制是人物0，1，2，-和=。在Windows上，您可以HumanPlayer用来调试提交。

决斗

这是通过控制台调试播放器的方法。

// Source.cpp
// An example duel between a HumanPlayer and GunClubPlayer.

#include "HumanPlayer.hpp"
#include "GunClubPlayer.hpp"
#include "GunDuel.hpp"

int main()
{
    // Total number of turns per duel.
    size_t duelLength = 100;

    // Player identifier 1: HumanPlayer.
    HumanPlayer human(2);
    // Player identifier 2: GunClubPlayer.
    GunClubPlayer gunClub(1);

    // Prepares a duel.
    GunDuel duel(human, gunClub, duelLength);
    // Start a duel.
    duel.fight();
}

游戏范例

您需要击败的回合数最少GunClubPlayer为3。这是与0-1对抗时的重放GunClubPlayer。括号中的数字是回合结束时每个玩家的弹药加载数量。

 :: Turn 0
    You [0/12/-=] >> [0] load ammo (1 ammo)
    Opponent selects [0] load ammo (1 ammo)
 :: Turn 1
    You [0/12/-=] >> [-] defend using metal shield (1 ammo)
    Opponent selects [1] fire a bullet (0 ammo)
 :: Turn 2
    You [0/12/-=] >> [1] fire a bullet (0 ammo)
    Opponent selects [0] load ammo (1 ammo)
 :: You won after 3 turns!
 :: Replay
    YOU 0-1
    FOE 010
Press any key to continue . . .

GunClubPlayer不做无效动作而战胜的最快方法是序列0=，因为子弹正好通过热​​导板射出。重播是

 :: Turn 0
    You [0/12/-=] >> [0] load ammo (1 ammo)
    Opponent selects [0] load ammo (1 ammo)
 :: Turn 1
    You [0/12/-=] >> [=] defend using thermal deflector (1 ammo)
    Opponent selects [1] fire a bullet (0 ammo)
 :: You lost after 2 turns!
 :: Replay
    YOU 0=
    FOE 01
Press any key to continue . . .

比赛

比赛遵循“最后一名选手排名”格式。在锦标赛中，所有有效的提交内容（包括GunClubPlayer）都放在一个池中。每个提交都分配有一个随机但唯一的标识符，该标识符在整个锦标赛中将保持不变。在每个回合中：

• 每个提交以0分开始，将与其他每个提交进行100决斗。
• 每个胜利的决斗将获得1分；画和输给0分。
• 在该回合结束时，以最低分提交的参赛者将离开比赛。如果出现平局，则自锦标赛开始以来获得积分最少的玩家将离开。
• 如果剩下一名以上的选手，则应开始下一轮比赛。
• 积分不会延续到下一轮。

投稿

您将为每个答案提交一名玩家。您可以为一个播放器提交多个文件，只要它们不干扰其他提交即可。为了保持顺畅，请执行以下操作：

• 将主头文件命名为<Custom>Player.hpp，
• 将其他文件命名为<Custom>Player*.*，例如，MyLittlePlayer.txt如果您的班级名称为MyLittlePlayer，或者EmoPlayerHates.cpp您的班级名称为EmoPlayer。
• 如果您的姓名包含Shooter或符合该比赛背景的类似单词，则无需Player在末尾添加。如果您强烈认为没有后缀的提交名称会更好Player，那么您也不需要添加Player。
• 确保您的代码可以在Windows下编译和链接。

您可以发表评论以要求澄清或发现漏洞。希望您喜欢这款未来派枪决斗，并祝您新年快乐！

澄清度

• 允许您有随机行为。
• 允许执行无效操作（加载弹药不足时触发）。
• 如果玩家输入的内容无效，他们的枪支将立即爆炸。
• 您可以研究答案。
• 明确允许您在每次锦标赛中记录对手的行为。
• 每回合，您将与每个对手进行100次决斗；但是，100个决斗的顺序是随机的-您不能保证连续打相同的对手100个决斗。

其他资源

如果您要提交C ++条目，@ flawr已将提供的C ++源代码翻译成Java作为参考。

非C ++提交的界面

目前已接受：Python 3，Java。

请遵循以下规范之一：

接口规范1：退出代码

您的提交每轮运行一次。

Expected Command Line Argument Format:
    <opponent-id> <turn> <status> <ammo> <ammo-opponent> <history> <history-opponent>

Expected Return Code: The ASCII value of a valid action character.
    '0' = 48, '1' = 49, '2' = 50, '-' = 45, '=' = 61

<opponent-id> is an integer in [0, N), where N is size of tournament.
<turn> is 0-based.
If duel is in progress, <status> is 3.
If duel is draw / won / lost, <status> is 0 / 1 / 2.
<history> and <history-opponent> are strings of actions, e.g. 002 0-=
If turn is 0, <history> and <history-opponent> are not provided.
You can ignore arguments you don't particularly need.

您可以在PythonPlayer\和JavaPlayer\目录中测试提交的内容。

接口规范2：stdin / stdout

（致谢H Walters）

您的提交将在每个锦标赛中运行一次。

由于stdin和stdout均已连接到锦标赛驱动程序，因此对如何执行I / O的所有条目都有固定的要求。违反此规定可能导致死锁。所有条目都必须遵循此EXACT算法（以伪代码）：

LOOP FOREVER
    READ LINE INTO L
    IF (LEFT(L,1) == 'I')
        INITIALIZE ROUND
        // i.e., set your/opponent ammo to 0, if tracking them
        // Note: The entire line at this point is a unique id per opponent;
        // optionally track this as well.
        CONTINUE LOOP
    ELSE IF (LEFT(L,1) == 'F')
        WRITELN F // where F is your move
    ELSE IF (LEFT(L,1) == 'P')
        PROCESS MID(L,2,1) // optionally perceive your opponent's action.
    END IF
CONTINUE LOOP
QUIT

这里，F是一0，1，2，-，或者=为load / bullet / plasma / metal / thermal。PROCESS意味着有选择地对对手的反应做出反应（如果您这样做的话，包括跟踪对手的弹药）。请注意，对手的动作也是“ 0”，“ 1”，“ 2”，“-”或“ =”之一，并且处于第二个字符中。

最终比分板

08:02 AM Tuesday, February 2, 2017 Coordinated Universal Time (UTC)
| Player             | Language   | Points |     1 |     2 |     3 |     4 |     5 |     6 |     7 |     8 |     9 |    10 |    11 |    12 |    13 |    14 |    15 |    16 |
|:------------------ |:---------- | ------:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:| -----:|
| MontePlayer        | C++        |  11413 |  1415 |  1326 |  1247 |  1106 |  1049 |   942 |   845 |   754 |   685 |   555 |   482 |   381 |   287 |   163 |   115 |    61 |
| CBetaPlayer        | C++        |   7014 |   855 |   755 |   706 |   683 |   611 |   593 |   513 |   470 |   414 |   371 |   309 |   251 |   192 |   143 |   109 |    39 |
| StudiousPlayer     | C++        |  10014 |  1324 |  1233 |  1125 |  1015 |   907 |   843 |   763 |   635 |   555 |   478 |   403 |   300 |   201 |   156 |    76 |
| FatedPlayer        | C++        |   6222 |   745 |   683 |   621 |   655 |   605 |   508 |   494 |   456 |   395 |   317 |   241 |   197 |   167 |   138 |
| HanSoloPlayer      | C++        |   5524 |   748 |   668 |   584 |   523 |   490 |   477 |   455 |   403 |   335 |   293 |   209 |   186 |   153 |
| SurvivorPlayer     | C++        |   5384 |   769 |   790 |   667 |   574 |   465 |   402 |   354 |   338 |   294 |   290 |   256 |   185 |
| SpecificPlayer     | C++        |   5316 |   845 |   752 |   669 |   559 |   488 |   427 |   387 |   386 |   340 |   263 |   200 |
| DeceptivePlayer    | C++        |   4187 |   559 |   445 |   464 |   474 |   462 |   442 |   438 |   369 |   301 |   233 |
| NotSoPatientPlayer | C++        |   5105 |   931 |   832 |   742 |   626 |   515 |   469 |   352 |   357 |   281 |
| BarricadePlayer    | C++        |   4171 |   661 |   677 |   614 |   567 |   527 |   415 |   378 |   332 |
| BotRobotPlayer     | C++        |   3381 |   607 |   510 |   523 |   499 |   496 |   425 |   321 |
| SadisticShooter    | C++        |   3826 |   905 |   780 |   686 |   590 |   475 |   390 |
| TurtlePlayer       | C++        |   3047 |   754 |   722 |   608 |   539 |   424 |
| CamtoPlayer        | C++        |   2308 |   725 |   641 |   537 |   405 |
| OpportunistPlayer  | C++        |   1173 |   426 |   420 |   327 |
| GunClubPlayer      | C++        |    888 |   500 |   388 |
| PlasmaPlayer       | C++        |    399 |   399 |

除非另有说明，否则比赛将持续到2017年2月1日。

蒙特播放器

该播放器使用“解耦的UCT蒙特卡洛树搜索”算法来决定应做出的选择。它跟踪敌人为预测其行动所做的事情。如果缺少数据，它将模拟敌人本身。

该机器人与除cβ之外的所有其他机器人相比都表现出色。在与cβ的10000决斗比赛中，蒙特赢得了5246决斗。有了一点点数学，这意味着Monte将会在51.17％到53.74％的时间（99％的置信度）中赢得cβ的对决。

#ifndef __Monte_PLAYER_HPP__
#define __Monte_PLAYER_HPP__

#include "Player.hpp"
#include <cstdlib>
#include <ctime>
#include <memory>
#include <iostream>


class MontePlayer final : public Player
{
    static const int MAX_TURNS = 100;
    static const int TOTAL_ACTIONS = 5;

    //Increase this if number of players goes above 20.
    static const int MAX_PLAYERS = 20;

    //The number of simulated games we run every time our program is called.
    static const int MONTE_ROUNDS = 1000;


    /**
    * Represents the current state of the game.
    */
    struct Game
    {
        int turn;
        int ammo;
        int opponentAmmo;
        bool alive;
        bool opponentAlive;

        Game(int turn, int ammo, int opponentAmmo, bool alive, bool opponentAlive)
            : turn(turn), ammo(ammo), opponentAmmo(opponentAmmo), alive(alive), opponentAlive(opponentAlive) {}
        Game() : turn(0), ammo(0), opponentAmmo(0), alive(false), opponentAlive(false) {}
    };

    struct Stat
    {
        int wins;
        int attempts;

        Stat() : wins(0), attempts(0) {}
    };

    /**
    * A Monte tree data structure.
    */
    struct MonteTree
    {
        //The state of the game.
        Game game;

        //myStats[i] returns the statistic for doing the i action in this state.
        Stat myStats[TOTAL_ACTIONS];
        //opponentStats[i] returns the statistic for the opponent doing the i action in this state.
        Stat opponentStats[TOTAL_ACTIONS];
        //Total number of times we've created statistics from this tree.
        int totalPlays = 0;
        //The action that led to this tree.
        int myAction;
        //The opponent action that led to this tree.
        int opponentAction;

        //The tree preceding this one.
        MonteTree *parent = NULL;

        //subtrees[i][j] is the tree that would follow if I did action i and the
        //opponent did action j.
        MonteTree *subtrees[TOTAL_ACTIONS][TOTAL_ACTIONS] = { { NULL } };

        MonteTree(int turn, int ammo, int opponentAmmo) :
            game(turn, ammo, opponentAmmo, true, true) {}


        MonteTree(Game game, MonteTree *parent, int myAction, int opponentAction) :
            game(game), parent(parent), myAction(myAction), opponentAction(opponentAction)
        {
            //Make sure the parent tree keeps track of this tree.
            parent->subtrees[myAction][opponentAction] = this;
        }

        //The destructor so we can avoid slow ptr types and memory leaks.
        ~MonteTree()
        {
            //Delete all subtrees.
            for (int i = 0; i < TOTAL_ACTIONS; i++)
            {
                for (int j = 0; j < TOTAL_ACTIONS; j++)
                {
                    auto branch = subtrees[i][j];

                    if (branch)
                    {
                        branch->parent = NULL;
                        delete branch;
                    }
                }
            }
        }
    };

    //The previous state.
    Game prevGame;
    //The id of the opponent.
    int opponent;
    //opponentHistory[a][b][c][d] returns the number of times
    //that opponent a did action d when I had b ammo and he had c ammo.
    static int opponentHistory[MAX_PLAYERS][MAX_TURNS][MAX_TURNS][TOTAL_ACTIONS];

public:
    MontePlayer(size_t opponent = -1) : Player(opponent)
    {
        srand(time(NULL));
        this->opponent = opponent;
    }

public:

    virtual Action fight()
    {
        //Create the root tree. Will be auto-destroyed after this function ends.
        MonteTree current(getTurn(), getAmmo(), getAmmoOpponent());

        //Set the previous game to this one.
        prevGame = current.game;

        //Get these variables so we can log later if nessecarry.
        int turn = getTurn(),
            ammo = getAmmo(),
            opponentAmmo = getAmmoOpponent();

        for (int i = 0; i < MONTE_ROUNDS; i++)
        {
            //Go down the tree until we find a leaf we haven't visites yet.
            MonteTree *leaf = selection(&current);

            //Randomly simulate the game at the leaf and get the result.
            int score = simulate(leaf->game);

            //Propagate the scores back up the root.
            update(leaf, score);
        }

        //Get the best move.
        int move = bestMove(current);

        //Move string for debugging purposes.
        const char* m;

        //We have to do this so our bots state is updated.
        switch (move)
        {
        case Action::LOAD:
            load();
            m = "load";
            break;
        case Action::BULLET:
            bullet();
            m = "bullet";
            break;
        case Action::PLASMA:
            plasma();
            m = "plasma";
            break;
        case Action::METAL:
            metal();
            m = "metal";
            break;
        case Action::THERMAL:
            thermal();
            m = "thermal";
            break;
        default: //???
            std::cout << move << " ???????\n";
            throw move;
        }

        return (Action)move;
    }

    /**
    * Record what the enemy does so we can predict him.
    */
    virtual void perceive(Action action)
    {
        Player::perceive(action);
        opponentHistory[opponent][prevGame.ammo][prevGame.opponentAmmo][action]++;
    }
private:

    /**
    * Trickle down root until we have to create a new leaf MonteTree or we hit the end of a game.
    */
    MonteTree * selection(MonteTree *root)
    {
        while (!atEnd(root->game))
        {
            //First pick the move that my bot will do.

            //The action my bot will do.
            int myAction;
            //The number of actions with the same bestScore.
            int same = 0;
            //The bestScore
            double bestScore = -1;

            for (int i = 0; i < TOTAL_ACTIONS; i++)
            {
                //Ignore invalid or idiot moves.
                if (!isValidMove(root->game, i, true))
                {
                    continue;
                }

                //Get the score for doing move i. Uses
                double score = computeScore(*root, i, true);

                //Randomly select one score if multiple actions have the same score.
                //Why this works is boring to explain.
                if (score == bestScore)
                {
                    same++;
                    if (Random(same) == 0)
                    {
                        myAction = i;
                    }
                }
                //Yay! We found a better action.
                else if (score > bestScore)
                {
                    same = 1;
                    myAction = i;
                    bestScore = score;
                }
            }

            //The action the enemy will do.
            int enemyAction;

            //The number of times the enemy has been in this same situation.
            int totalEnemyEncounters = 0;
            for (int i = 0; i < TOTAL_ACTIONS; i++)
            {
                totalEnemyEncounters += opponentHistory[opponent][root->game.ammo][root->game.opponentAmmo][i];
            }

            //Assume the enemy will choose an action it has chosen before if we've
            //seen it in this situation before. Otherwise we assume that the enemy is ourselves.
            if (totalEnemyEncounters > 0)
            {
                //Randomly select an action that the enemy has done with
                //weighted by the number of times that action has been done.
                int selection = Random(totalEnemyEncounters);
                for (int i = 0; i < TOTAL_ACTIONS; i++)
                {
                    selection -= opponentHistory[opponent][root->game.ammo][root->game.opponentAmmo][i];
                    if (selection < 0)
                    {
                        enemyAction = i;
                        break;
                    }
                }
            }
            else
            {
                //Use the same algorithm to pick the enemies move we use for ourselves.
                same = 0;
                bestScore = -1;
                for (int i = 0; i < TOTAL_ACTIONS; i++)
                {
                    if (!isValidMove(root->game, i, false))
                    {
                        continue;
                    }

                    double score = computeScore(*root, i, false);
                    if (score == bestScore)
                    {
                        same++;
                        if (Random(same) == 0)
                        {
                            enemyAction = i;
                        }
                    }
                    else if (score > bestScore)
                    {
                        same = 1;
                        enemyAction = i;
                        bestScore = score;
                    }
                }
            }

            //If this combination of actions hasn't been explored yet, create a new subtree to explore.
            if (!(*root).subtrees[myAction][enemyAction])
            {
                return expand(root, myAction, enemyAction);
            }

            //Do these actions and explore the next subtree.
            root = (*root).subtrees[myAction][enemyAction];
        }
        return root;
    }

    /**
    * Creates a new leaf under root for the actions.
    */
    MonteTree * expand(MonteTree *root, int myAction, int enemyAction)
    {
        return new MonteTree(
            doTurn(root->game, myAction, enemyAction),
            root,
            myAction,
            enemyAction);
    }

    /**
    * Computes the score of the given move in the given position.
    * Uses the UCB1 algorithm and returns infinity for moves not tried yet.
    */
    double computeScore(const MonteTree &root, int move, bool me)
    {
        const Stat &stat = me ? root.myStats[move] : root.opponentStats[move];
        return stat.attempts == 0 ?
            HUGE_VAL :
            double(stat.wins) / stat.attempts + sqrt(2 * log(root.totalPlays) / stat.attempts);
    }

    /**
    * Randomly simulates the given game.
    * Has me do random moves that are not stupid.
    * Has opponent do what it has done in similar positions or random moves if not
    * observed in those positions yet.
    *
    * Returns 1 for win. 0 for loss. -1 for draw.
    */
    int simulate(Game game)
    {
        while (!atEnd(game))
        {
            game = doRandomTurn(game);
        }

        if (game.alive > game.opponentAlive)
        {
            return 1;
        }
        else if (game.opponentAlive > game.alive)
        {
            return 0;
        }
        else //Draw
        {
            return -1;
        }
    }

    /**
    * Returns whether the game is over or not.
    */
    bool atEnd(Game game)
    {
        return !game.alive || !game.opponentAlive || game.turn > MAX_TURNS;
    }

    /**
    * Simulates the given actions on the game.
    */
    Game doTurn(Game game, int myAction, int enemyAction)
    {
        game.turn++;

        switch (myAction)
        {
        case Action::LOAD:
            game.ammo++;
            break;
        case Action::BULLET:
            if (game.ammo < 1)
            {
                game.alive = false;
                break;
            }
            game.ammo--;
            if (enemyAction == Action::LOAD || enemyAction == Action::THERMAL)
            {
                game.opponentAlive = false;
            }
            break;
        case Action::PLASMA:
            if (game.ammo < 2)
            {
                game.alive = false;
                break;
            }
            game.ammo -= 2;
            if (enemyAction != Action::PLASMA && enemyAction != Action::THERMAL)
            {
                game.opponentAlive = false;
            }
            break;
        }

        switch (enemyAction)
        {
        case Action::LOAD:
            game.opponentAmmo++;
            break;
        case Action::BULLET:
            if (game.opponentAmmo < 1)
            {
                game.opponentAlive = false;
                break;
            }
            game.opponentAmmo--;
            if (myAction == Action::LOAD || myAction == Action::THERMAL)
            {
                game.alive = false;
            }
            break;
        case Action::PLASMA:
            if (game.opponentAmmo < 2)
            {
                game.opponentAlive = false;
            }
            game.opponentAmmo -= 2;
            if (myAction != Action::PLASMA && myAction != Action::THERMAL)
            {
                game.alive = false;
            }
            break;
        }

        return game;
    }

    /**
    * Chooses a random move for me and my opponent and does it.
    */
    Game doRandomTurn(Game &game)
    {
        //Select my random move.
        int myAction;
        int validMoves = 0;

        for (int i = 0; i < TOTAL_ACTIONS; i++)
        {
            //Don't do idiotic moves.
            //Select one at random.
            if (isValidMove(game, i, true))
            {
                validMoves++;
                if (Random(validMoves) == 0)
                {
                    myAction = i;
                }
            }
        }

        //Choose random opponent action.
        int opponentAction;

        //Whether the enemy has encountered this situation before
        bool enemyEncountered = false;

        validMoves = 0;

        //Weird algorithm that works and I don't want to explain.
        //What it does:
        //If the enemy has encountered this position before,
        //then it chooses a random action weighted by how often it did that action.
        //If they haven't, makes the enemy choose a random not idiot move.
        for (int i = 0; i < TOTAL_ACTIONS; i++)
        {
            int weight = opponentHistory[opponent][game.ammo][game.opponentAmmo][i];
            if (weight > 0)
            {
                if (!enemyEncountered)
                {
                    enemyEncountered = true;
                    validMoves = 0;
                }
                validMoves += weight;
                if (Random(validMoves) < weight)
                {
                    opponentAction = i;
                }
            }
            else if (!enemyEncountered && isValidMove(game, i, false))
            {
                validMoves++;
                if (Random(validMoves) == 0)
                {
                    opponentAction = i;
                }
            }
        }

        return doTurn(game, myAction, opponentAction);
    }

    /**
    * Returns whether the given move is valid/not idiotic for the game.
    */
    bool isValidMove(Game game, int move, bool me)
    {
        switch (move)
        {
        case Action::LOAD:
            return true;
        case Action::BULLET:
            return me ? game.ammo > 0 : game.opponentAmmo > 0;
        case Action::PLASMA:
            return me ? game.ammo > 1 : game.opponentAmmo > 1;
        case Action::METAL:
            return me ? game.opponentAmmo > 0 : game.ammo > 0;
        case Action::THERMAL:
            return me ? game.opponentAmmo > 1 : game.ammo > 1;
        default:
            return false;
        }
    }

    /**
    * Propagates the score up the MonteTree from the leaf.
    */
    void update(MonteTree *leaf, int score)
    {
        while (true)
        {
            MonteTree *parent = leaf->parent;
            if (parent)
            {
                //-1 = draw, 1 = win for me, 0 = win for opponent
                if (score != -1)
                {
                    parent->myStats[leaf->myAction].wins += score;
                    parent->opponentStats[leaf->opponentAction].wins += 1 - score;
                }
                parent->myStats[leaf->myAction].attempts++;
                parent->opponentStats[leaf->opponentAction].attempts++;
                parent->totalPlays++;
                leaf = parent;
            }
            else
            {
                break;
            }
        }
    }

    /**
    * There are three different strategies in here.
    * The first is not random, the second more, the third most.
    */
    int bestMove(const MonteTree &root)
    {
        //Select the move with the highest win rate.
        int best;
        double bestScore = -1;
        for (int i = 0; i < TOTAL_ACTIONS; i++)
        {
            if (root.myStats[i].attempts == 0)
            {
                continue;
            }

            double score = double(root.myStats[i].wins) / root.myStats[i].attempts;
            if (score > bestScore)
            {
                bestScore = score;
                best = i;
            }
        }

        return best;

        ////Select a move weighted by the number of times it has won the game.
        //int totalScore = 0;
        //for (int i = 0; i < TOTAL_ACTIONS; i++)
        //{
        //  totalScore += root.myStats[i].wins;
        //}
        //int selection = Random(totalScore);
        //for (int i = 0; i < TOTAL_ACTIONS; i++)
        //{
        //  selection -= root.myStats[i].wins;
        //  if (selection < 0)
        //  {
        //      return i;
        //  }
        //}

        ////Select a random move weighted by win ratio.
        //double totalScore = 0;
        //for (int i = 0; i < TOTAL_ACTIONS; i++)
        //{
        //  if (root.myStats[i].attempts == 0)
        //  {
        //      continue;
        //  }
        //  totalScore += double(root.myStats[i].wins) / root.myStats[i].attempts;
        //}
        //double selection = Random(totalScore);
        //for (int i = 0; i < TOTAL_ACTIONS; i++)
        //{
        //  if (root.myStats[i].attempts == 0)
        //  {
        //      continue;
        //  }
        //  selection -= double(root.myStats[i].wins) / root.myStats[i].attempts;
        //  if (selection < 0)
        //  {
        //      return i;
        //  }
        //}
    }

    //My own random functions.
    int Random(int max)
    {
        return GetRandomInteger(max - 1);
    }
    double Random(double max)
    {
        static auto seed = std::chrono::system_clock::now().time_since_epoch().count();
        static std::default_random_engine generator((unsigned)seed);
        std::uniform_real_distribution<double> distribution(0.0, max);
        return distribution(generator);
    }
};
//We have to initialize this here for some reason.
int MontePlayer::opponentHistory[MAX_PLAYERS][MAX_TURNS][MAX_TURNS][TOTAL_ACTIONS]{ { { { 0 } } } };

#endif // !__Monte_PLAYER_HPP__

的 BlackHatPlayer

黑帽玩家知道子弹和盾牌已经成为过去。那些能够破解对手程序的人赢得了真正的战争。

因此，他戴上了固定的金属护罩，开始做他的事情。

第一次被要求时fight，他试图将他的敌人定位在记忆中。考虑到战斗场的结构，几乎可以肯定，编译器最终将把他的地址（包裹在中unique_ptr）和一个对手放在另一个位置。

因此，BlackHat使用一些简单的试探法仔细地检查堆栈，以确保在发现指向自己的指针之前不会使其溢出。然后检查相邻位置中的值是否合理地是他的对手-相似地址，vtable的相似地址是否合理typeid。

如果设法找到他，他会动脑筋，用一个傻瓜白痴代替他们。实际上，这是通过将对手指向vtable的指针替换为vtable的地址来完成的。Idiot vtable是一个总是射击的愚蠢玩家。

如果这一切都成功了（在我的测试中-Linux 64位上的gcc 6，葡萄酒32位上的MinGW 4.8-这确实很可靠），那么战争就赢了。对手在第一轮中所做的一切都不重要-最坏的是他开枪打了我们，我们戴上了金属护罩。

从现在开始，我们有一个白痴只是射击；我们始终会戴上盾牌，因此我们会受到保护，他会在1到3轮后炸毁（取决于原始漫游器在他的第一个fight呼叫中所做的操作）。

现在：我几乎确定应该立即取消其资格，但有趣的是，我没有明确违反上述任何规则：

你不能做什么

• 除了给定的对手标识符外，您不得使用任何直接方法来识别您的对手，该标识符在每次锦标赛开始时都是完全随机的。您只能通过锦标赛中的玩法来猜测谁是谁。

BlackHat不会尝试识别对手-实际上，鉴于对手的大脑立即被替换，这与对手的身份完全无关。

• 您不得覆盖Player类中未声明为虚拟的任何方法。
• 您不得在全局范围内声明或初始化任何内容。

一切都在fight虚拟功能本地发生。

// BlackHatPlayer.hpp

#ifndef __BLACKHAT_PLAYER_HPP__
#define __BLACKHAT_PLAYER_HPP__

#include "Player.hpp"
#include <stddef.h>
#include <typeinfo>
#include <algorithm>
#include <string.h>

class BlackHatPlayer final : public Player
{
public:
    using Player::Player;

    virtual Action fight()
    {
        // Always metal; if the other is an Idiot, he only shoots,
        // and if he isn't an Idiot yet (=first round) it's the only move that
        // is always safe
        if(tricked) return metal();
        // Mark that at the next iterations we don't have to do all this stuff
        tricked = true;

        typedef uintptr_t word;
        typedef uintptr_t *pword;
        typedef uint8_t *pbyte;

        // Size of one memory page; we use it to walk the stack carefully
        const size_t pageSize = 4096;
        // Maximum allowed difference between the vtables
        const ptrdiff_t maxVTblDelta = 65536;
        // Maximum allowed difference between this and the other player
        ptrdiff_t maxObjsDelta = 131072;

        // Our adversary
        Player *c = nullptr;

        // Gets the start address of the memory page for the given object
        auto getPage = [&](void *obj) {
            return pword(word(obj) & (~word(pageSize-1)));
        };
        // Gets the start address of the memory page *next* to the one of the given object
        auto getNextPage = [&](void *obj) {
            return pword(pbyte(getPage(obj)) + pageSize);
        };

        // Gets a pointer to the first element of the vtable
        auto getVTbl = [](void *obj) {
            return pword(pword(obj)[0]);
        };

        // Let's make some mess to make sure that:
        // - we have an actual variable on the stack;
        // - we call an external (non-inline) function that ensures everything
        //   is spilled on the stack
        // - the compiler actually generates the full vtables (in the current
        //   tournament this shouldn't be an issue, but in earlier sketches
        //   the compiler inlined everything and killed the vtables)
        volatile word i = 0;
        for(const char *sz = typeid(*(this+i)).name(); *sz; ++sz) i+=*sz;

        // Grab my vtable
        word *myVTbl = getVTbl(this);

        // Do the stack walk
        // Limit for the stack walk; use i as a reference
        word *stackEnd = getNextPage((pword)(&i));
        for(word *sp = pword(&i);       // start from the location of i
            sp!=stackEnd && c==nullptr;
            ++sp) {                     // assume that the stack grows downwards
            // If we find something that looks like a pointer to memory
            // in a page just further on the stack, take it as a clue that the
            // stack in facts does go on
            if(getPage(pword(*sp))==stackEnd) {
                stackEnd = getNextPage(pword(*sp));
            }
            // We are looking for our own address on the stack
            if(*sp!=(word)this) continue;

            auto checkCandidate = [&](void *candidate) -> Player* {
                // Don't even try with NULLs and the like
                if(getPage(candidate)==nullptr) return nullptr;
                // Don't trust objects too far away from us - it's probably something else
                if(abs(pbyte(candidate)-pbyte(this))>maxObjsDelta) return nullptr;
                // Grab the vtable, check if it actually looks like one (it should be
                // decently near to ours)
                pword vtbl = getVTbl(candidate);
                if(abs(vtbl-myVTbl)>maxVTblDelta) return nullptr;
                // Final check: try to see if its name looks like a "Player"
                Player *p = (Player *)candidate;
                if(strstr(typeid(*p).name(), "layer")==0) return nullptr;
                // Jackpot!
                return p;
            };

            // Look around us - a pointer to our opponent should be just near
            c = checkCandidate((void *)sp[-1]);
            if(c==nullptr) c=checkCandidate((void *)sp[1]);
        }

        if(c!=nullptr) {
            // We found it! Suck his brains out and put there the brains of a hothead idiot
            struct Idiot : Player {
                virtual Action fight() {
                    // Always fire, never reload; blow up in two turns
                    // (while we are always using the metal shield to protect ourselves)
                    return bullet();
                }
            };
            Idiot idiot;
            // replace the vptr
            (*(word *)(c)) = word(getVTbl(&idiot));
        }
        // Always metal shield to be protected from the Idiot
        return metal();
    }
private:
    bool tricked = false;
};

#endif // !__BLACKHAT_PLAYER_HPP__

接下来，在所有生物中最害怕的是，它已经下地狱并与其他900000个其他机器人进行过战斗，它的...

的 BotRobot

BotRobot是通过非常基本的遗传算法自动命名，训练和构建的。

由9人组成的两支队伍互相对抗，每一代中，第1队的每个机器人都与第2队的每个机器人对抗。获胜多于损失的机器人保留了记忆，另一个又回到了最后一步，并有机会忘记一些东西，希望是不好的。僵尸程序本身是经过修饰的查找表，如果它们找到了以前从未见过的内容，则只需选择一个随机有效选项并将其保存到内存中即可。C ++版本不这样做，它应该已经了解。如前所述，成功的机器人会保留这些新发现的内存，这显然很有效。失去的机器人不会，并保持最初的状态。

最后，僵尸网络的战斗非常接近，很少陷入僵局。获胜者被从游泳池中挑选出来两队的演变后，这是几代100000。

BotRobot 是一个幸运的名字，它具有随机生成的美丽名称。

发电机

bot.lua

修订： 尽管该机器人对自己和其他类似生成的机器人相当聪明，但在实际战斗中却被证明毫无用处。因此，我针对一些已经创建的机器人重振了他的大脑。

可以很容易地看出，结果是大脑要复杂得多，有多达12个弹药的敌人玩家选择。

我不确定他在和那架战斗达到12弹药的目标，但确实有所作为。

当然，成品...

// BotRobot
// ONE HUNDRED THOUSAND GENERATIONS TO MAKE THE ULTIMATE LIFEFORM!

#ifndef __BOT_ROBOT_PLAYER_HPP__
#define __BOT_ROBOT_PLAYER_HPP__

#include "Player.hpp"

class BotRobotPlayer final : public Player
{
public:
    BotRobotPlayer(size_t opponent = -1) : Player(opponent) {}

public:
    virtual Action fight()
    {
        std::string action = "";
        action += std::to_string(getAmmo());
        action += ":";
        action += std::to_string(getAmmoOpponent());

        int toDo = 3;

        for (int i = 0; i < int(sizeof(options)/sizeof(*options)); i++) {
            if (options[i].compare(action)==0) {
                toDo = outputs[i];
                break;
            }
        }

        switch (toDo) {
            case 0:
                return load();
            case 1:
                return bullet();
            case 2:
                return plasma();
            case 3:
                return metal();
            default:
                return thermal();
        }
    }

private:
    std::string options[29] =
    {
        "0:9",
        "1:12",
        "1:10",
        "0:10",
        "1:11",
        "0:11",
        "0:6",
        "2:2",
        "0:2",
        "2:6",
        "3:6",
        "0:7",
        "1:3",
        "2:3",
        "0:3",
        "2:0",
        "1:0",
        "0:4",
        "1:4",
        "2:4",
        "0:0",
        "3:0",
        "1:1",
        "2:1",
        "2:9",
        "0:5",
        "0:8",
        "3:1",
        "0:1"
    };

    int outputs[29] =
    {
        0,
        1,
        1,
        4,
        1,
        0,
        0,
        4,
        4,
        0,
        0,
        3,
        0,
        1,
        3,
        0,
        1,
        4,
        0,
        1,
        0,
        1,
        0,
        3,
        4,
        3,
        0,
        1,
        0
    };
};

#endif // !__BOT_ROBOT_PLAYER_HPP__

我现在讨厌 C ++ ...

CBetaPlayer （cβ）

近似纳什均衡。

这个机器人只是带有代码包装器的精美数学工具。

我们可以将其重新构建为博弈论问题。用+1表示赢，用-1表示亏损。现在让B（x，y）为游戏的值，其中我们有x弹药，而对手有y弹药。请注意，B（a，b）= -B（b，a），因此B（a，a）=0。要根据其他B值找到B值，我们可以计算支付矩阵的值。例如，我们有以下子游戏的值给定B（1，0）：

       load      metal
load    B(0, 1)   B(2, 0)
bullet  +1        B(0, 0)

（我已经删除了“不良”选项，也就是那些已被现有解决方案严格控制的选项。例如，由于只有1个弹药，我们不会尝试发射等离子。同样，我们的对手也不会使用热导板，因为我们永远不会发射等离子体。）

博弈论让我们知道，在某些技术条件下，如何找到该收益矩阵的值。我们得到上述矩阵的值为：

                B(2, 0)
B(1, 0) = ---------------------
          1 + B(2, 0) - B(2, 1)

继续进行所有可能的游戏，并注意B（x，y）-> 1为x->固定y的无穷大，我们可以找到所有B值，从而可以计算出完美的移动！

当然，理论很少与现实相吻合。快速求解甚至很小的x和y的方程变得太复杂。为了解决这个问题，我介绍了我所谓的cβ近似。此近似值有7个参数：c0，β0，c1，β1，c，β和k。我假设B值采用以下形式（首先是最具体的形式）：

B(1, 0) = k
B(x, 0) = 1 - c0 β0^x
B(x, 1) = 1 - c1 β1^x
B(x, y) = 1 - c β^(x - y)   (if x > y)

关于为什么选择这些参数的一些粗略推论。首先，我知道我绝对想分别处理0、1和2或更多的弹药，因为每种弹药都有不同的选择。我还认为几何生存函数将是最合适的，因为防守球员本质上是在猜测该采取什么行动。我发现拥有2个或更多弹药基本上是相同的，因此我专注于差异。我还想将B（1，0）视为超级特例，因为我认为它会出现很多。使用这些近似形式可以大大简化B值的计算。

我近似地求解了所得方程，以获得每个B值，然后将其放回矩阵中以获取收益矩阵。然后，使用线性编程求解器，我发现了进行每步移动的最佳概率，并将其推入程序。

该程序是一个美化的查找表。如果两个玩家的弹药都在0到4之间，它会使用概率矩阵随机确定应该采取的行动。否则，它将尝试根据其表进行推断。

它对笨拙的确定性机器人有一定的麻烦，但对理性机器人却效果很好。由于所有近似值，有时它确实不应该输给StudiousPlayer。

当然，如果我要再次执行此操作，则可能会尝试添加更多独立参数或更好的ansatz形式，并提出更精确的解决方案。另外，我（故意）忽略了转弯限制，因为这使事情变得更加困难。如果我们有足够的弹药并且没有足够的转弯，可以进行快速修改以始终发射等离子体。

// CBetaPlayer (cβ)
// PPCG: George V. Williams

#ifndef __CBETA_PLAYER_HPP__
#define __CBETA_PLAYER_HPP__

#include "Player.hpp"
#include <iostream>

class CBetaPlayer final : public Player
{
public:
    CBetaPlayer(size_t opponent = -1) : Player(opponent)
    {
    }

public:
    virtual Action fight()
    {
        int my_ammo = getAmmo(), opp_ammo = getAmmoOpponent();

        while (my_ammo >= MAX_AMMO || opp_ammo >= MAX_AMMO) {
            my_ammo--;
            opp_ammo--;
        }

        if (my_ammo < 0) my_ammo = 0;
        if (opp_ammo < 0) opp_ammo = 0;

        double cdf = GetRandomDouble();
        int move = -1;
        while (cdf > 0 && move < MAX_MOVES - 1)
            cdf -= probs[my_ammo][opp_ammo][++move];

        switch (move) {
            case 0: return load();
            case 1: return bullet();
            case 2: return plasma();
            case 3: return metal();
            case 4: return thermal();
            default: return fight();
        }
    }

    static double GetRandomDouble() {
        static auto seed = std::chrono::system_clock::now().time_since_epoch().count();
        static std::default_random_engine generator((unsigned)seed);
        std::uniform_real_distribution<double> distribution(0.0, 1.0);
        return distribution(generator);
    }

private:
    static const int MAX_AMMO = 5;
    static const int MAX_MOVES = 5;

    double probs[MAX_AMMO][MAX_AMMO][5] =
        {
            {{1, 0, 0, 0, 0}, {0.58359, 0, 0, 0.41641, 0}, {0.28835, 0, 0, 0.50247, 0.20918}, {0.17984, 0, 0, 0.54611, 0.27405}, {0.12707, 0, 0, 0.56275, 0.31018}},
            {{0.7377, 0.2623, 0, 0, 0}, {0.28907, 0.21569, 0, 0.49524, 0}, {0.0461, 0.06632, 0, 0.53336, 0.35422}, {0.06464, 0.05069, 0, 0.43704, 0.44763}, {0.02215, 0.038, 0, 0.33631, 0.60354}},
            {{0.47406, 0.37135, 0.1546, 0, 0}, {0.1862, 0.24577, 0.15519, 0.41284, 0}, {0, 0.28343, 0.35828, 0, 0.35828}, {0, 0.20234, 0.31224, 0, 0.48542}, {0, 0.12953, 0.26546, 0, 0.605}},
            {{0.33075, 0.44563, 0.22362, 0, 0}, {0.17867, 0.20071, 0.20071, 0.41991, 0}, {0, 0.30849, 0.43234, 0, 0.25916}, {0, 0.21836, 0.39082, 0, 0.39082}, {0, 0.14328, 0.33659, 0, 0.52013}},
            {{0.24032, 0.48974, 0.26994, 0, 0}, {0.14807, 0.15668, 0.27756, 0.41769, 0}, {0, 0.26804, 0.53575, 0, 0.19621}, {0, 0.22106, 0.48124, 0, 0.2977}, {0, 0.15411, 0.42294, 0, 0.42294}}
        };


};

#endif // !__CBETA_PLAYER_HPP__

我到处都缺少评论，所以我还不能提出问题。因此，这是赢得第一个机器人的非常基本的玩家。

[编辑]谢谢，现在以前的状态已经不正确了，但是我认为最好保留它，这样我们才能了解此bot的上下文。

的 Opportunist

机会主义者经常与GunClubPlayers参加同一个枪支俱乐部，但是，他向一个新来者打赌，他可以击败所有GunClubPlayers。因此，他利用了他早已注意到的习惯，强迫自己不要开枪，而要稍等一下才能获胜。

#ifndef __OPPORTUNIST_PLAYER_HPP__
#define __OPPORTUNIST_PLAYER_HPP__

#include <string>
#include <vector>

class OpportunistPlayer final: public Player
{
public:
    OpportunistPlayer(size_t opponent = -1) : Player(opponent) {}

public:
    virtual Action fight()
    {
        switch (getTurn() % 3)
        {
        case 0:
            return load();
            break;
        case 1:
            return metal();
            break;
        case 2:
            return bullet();
            break;
        }
        return plasma();
    }
};
#endif // !__OPPORTUNIST_PLAYER_HPP__

的 BarricadePlayer

街垒玩家会在第一轮加载子弹，然后保留适当的盾牌（仍然有点随机）。他还每5轮加注一次。每回合，都有15％的机会忽略算法（除了第一回合重新加载）并发射子弹。当敌人没有弹药时，它会加载。如果某种方式出问题了，天哪，他开枪了。

最新变化：

改进了随机数（感谢Frenzy Li）。

// BarricadePlayer by devRicher
// PPCG: http://codegolf.stackexchange.com/a/104909/11933

// BarricadePlayer.hpp
// A very tactical player.

#ifndef __BARRICADE_PLAYER_HPP__
#define __BARRICADE_PLAYER_HPP__

#include "Player.hpp"
#include <cstdlib>
#include <ctime>

class BarricadePlayer final : public Player
{
public:
    BarricadePlayer(size_t opponent = -1) : Player(opponent) {}

public:
    virtual Action fight()
    {
        srand(time(NULL));
        if (getTurn() == 0) { return load(); }
        int r = GetRandomInteger(99) + 1; //Get a random
        if ((r <= 15) && (getAmmo() > 0)) { return bullet(); } //Override any action, and just shoot
        else
        {
            if (getTurn() % 5 == 0) //Every first and fifth turn
                return load();
            if (getAmmoOpponent() == 1) return metal();
            if (getAmmoOpponent() > 1) { return r <= 50 ? metal() : thermal(); }
            if (getAmmoOpponent() == 0) return load();

        }
        return bullet();
    }
};

#endif // !__BARRICADE_PLAYER_HPP__

的 StudiousPlayer

勤奋的玩家会研究猎物，为遇到的每个对手建模。该玩家从基本策略开始，随机驱动到位，然后根据对手反应的频繁性测度发展为简单的自适应策略。它根据对手对弹药组合的反应使用简单的对手模型。

#ifndef __STUDIOUS_PLAYER_H__
#define __STUDIOUS_PLAYER_H__

#include "Player.hpp"
#include <unordered_map>

class StudiousPlayer final : public Player
{
public:
   using Player::GetRandomInteger;
   // Represents an opponent's action for a specific state.
   struct OpponentAction {
      OpponentAction(){}
      unsigned l=0;
      unsigned b=0;
      unsigned p=0;
      unsigned m=0;
      unsigned t=0;
   };
   // StudiousPlayer models every opponent that it plays,
   // and factors said model into its decisions.
   //
   // There are 16 states, corresponding to
   // 4 inner states (0,1,2,3) and 4 outer states
   // (0,1,2,3). The inner states represent our
   // (SP's) ammo; the outer represents the
   // Opponent's ammo.  For the inner or outer
   // states, 0-2 represent the exact ammo; and
   // 3 represents "3 or more".
   //
   // State n is (4*outer)+inner.
   //
   // State 0 itself is ignored, since we don't care
   // what action the opponent takes (we always load);
   // thus, it's not represented here.
   //
   // os stores states 1 through 15 (index 0 through 14).
   struct Opponent {
      std::vector<OpponentAction> os;
      Opponent() : os(15) {}
   };
   StudiousPlayer(size_t opponent)
      : Player(opponent)
      , strat(storedLs()[opponent])
      , ammoOpponent()
   {
   }
   Player::Action fight() {
      // Compute the current "ammo state".
      // For convenience here (aka, readability in switch),
      // this is a two digit octal number.  The lso is the
      // inner state, and the mso the outer state.
      unsigned ss,os;
      switch (ammoOpponent) {
      default: os=030; break;
      case 2 : os=020; break;
      case 1 : os=010; break;
      case 0 : os=000; break;
      }
      switch (getAmmo()) {
      default: ss=003; break;
      case 2 : ss=002; break;
      case 1 : ss=001; break;
      case 0 : ss=000; break;
      }
      // Store the ammo state.  This has a side effect
      // of causing actn() to return an OpponentAction
      // struct, with the opponent's history during this
      // state.
      osa = os+ss;
      // Get the opponent action pointer
      const OpponentAction* a=actn(osa);
      // If there's no such action structure, assume
      // we're just supposed to load.
      if (!a) return load();
      // Apply ammo-state based strategies:
      switch (osa) {
      case 001:
         // If opponent's likely to load, shoot; else load
         if (a->l > a->m) return bullet();
         return load();
      case 002:
      case 003:
         // Shoot in the way most likely to kill (or randomly)
         if (a->t > a->m+a->l) return bullet();
         if (a->m > a->t+a->l) return plasma();
         if (GetRandomInteger(1)) return bullet();
         return plasma();
      case 010:
         // If opponent tends to load, load; else defend
         if (a->l > a->b) return load();
         return metal();
      case 011:
         // Shoot if opponent tends to load
         if (a->l > a->b+a->m) return bullet();
         // Defend if opponent tends to shoot
         if (a->b > a->l+a->m) return metal();
         // Load if opponent tends to defend
         if (a->m > a->b+a->l) return load();
         // Otherwise randomly respond
         if (!GetRandomInteger(2)) return metal();
         if (!GetRandomInteger(1)) return load(); 
         return bullet();                         
      case 012:
      case 013:
         // If opponent most often shoots, defend
         if (a->b > a->l+a->m+a->t) return metal();
         // If opponent most often thermals, use bullet
         if (a->t > a->m) return bullet();
         // If opponent most often metals, use plasma
         if (a->m > a->t) return plasma();
         // Otherwise use a random weapon
         return (GetRandomInteger(1))?bullet():plasma();
      case 020:
         // If opponent most often loads or defends, load
         if (a->l+a->m+a->t > a->b+a->p) return load();
         // If opponent most often shoots bullets, raise metal
         if (a->b > a->p) return metal();
         // If opponent most often shoots plasma, raise thermal
         if (a->p > a->b) return thermal();
         // Otherwise raise random defense
         return (GetRandomInteger(1))?metal():thermal();
      case 021:
      case 031:
         // If opponent loads more often than not,
         if (a->l > a->m+a->b+a->p) {
            // Tend to shoot (67%), but possibly load (33%)
            return (GetRandomInteger(2))?bullet():load();
         }
         // If opponent metals more often than loads or shoots, load
         if (a->m > a->l+a->b+a->p) return load();
         // If opponent thermals (shrug) more often than loads or shoots, load
         if (a->t > a->l+a->b+a->p) return load();
         // If opponent tends to shoot bullets, raise metal
         if (a->b > a->p) return metal();
         // If opponent tends to shoot plasma, raise thermal
         if (a->p > a->b) return thermal();
         // Raise random shield
         return (GetRandomInteger(2))?metal():thermal();
      case 022:
         // If opponent loads or thermals more often than not, shoot bullet
         if (a->l+a->t > a->b+a->p+a->m) return bullet();
         // If opponent loads or metals more often than not, shoot plasma
         if (a->l+a->m > a->b+a->p+a->t) return plasma();
         // If opponent shoots more than loads or defends, defend
         if (a->b+a->p > a->l+a->m+a->t) {
            if (a->b > a->p) return metal();
            if (a->p > a->b) return thermal();
            return (GetRandomInteger(1))?metal():thermal();
         }
         // If opponent defends more than opponent shoots, load
         if (a->m+a->t > a->b+a->p) return load();
         // Use random substrategy;
         // load(33%)
         if (GetRandomInteger(2)) return load();
         // defend(33%)
         if (GetRandomInteger(1)) {
            if (a->b > a->p) return metal();
            if (a->b > a->b) return thermal();
            return (GetRandomInteger(1))?metal():thermal();
         }
         // Shoot in a way that most often kills (or randomly)
         if (a->m > a->t) return plasma();
         if (a->t > a->m) return bullet();
         return (GetRandomInteger(1))?bullet():plasma();
      case 023:
         // If opponent loads or raises thermal more often than not, shoot bullets
         if (a->l+a->t > a->b+a->p+a->m) return bullet();
         // If opponent loads or raises metal more often than not, shoot plasma
         if (a->l+a->m > a->b+a->p+a->t) return plasma();
         // If opponent shoots more than loads or defends, defend
         if (a->b+a->p > a->l+a->m+a->t) {
            if (a->b > a->p) return metal();
            if (a->p > a->b) return thermal();
            return (GetRandomInteger(1))?metal():thermal();
         }
         // If opponent defends more than shoots, shoot
         if (a->m+a->t > a->b+a->p) {
            if (a->m > a->t) return plasma();
            if (a->t > a->m) return bullet();
            return GetRandomInteger(1)?bullet():plasma();
         }
         // 50% defend
         if (GetRandomInteger(1)) {
            if (a->b > a->p) return metal();
            return thermal();
         }
         // 50% shoot
         if (a->m > a->t) return plasma();
         if (a->t > a->m) return bullet();
         return (GetRandomInteger(1))?bullet():plasma();
      case 030:
         // If opponent loads or shields more often than not, load
         if (a->l+a->m+a->t > a->b+a->p) return load();
         // If opponent tends to shoot, defend
         if (a->b+a->p >= a->l+a->m+a->t) {
            if (a->b > a->p) return metal();
            if (a->p > a->b) return thermal();
            return (GetRandomInteger(1))?metal():thermal();
         }
         // Otherwise, randomly shield (50%) or load
         if (GetRandomInteger(1)) {
            return (GetRandomInteger(1))?metal():thermal();
         }
         return load();
      case 032:
         // If opponent loads or thermals more often than not, shoot bullets
         if (a->l+a->t > a->b+a->p+a->m) return bullet();
         // If opponent loads or metals more often than not, shoot plasma
         if (a->l+a->m > a->b+a->p+a->t) return plasma();
         // If opponent shoots more often than loads or shields, defend
         if (a->b+a->p > a->l+a->m+a->t) {
            if (a->b > a->p) return metal();
            if (a->p > a->b) return thermal();
            return (GetRandomInteger(1))?metal():thermal();
         }
         // If opponent shields more often than shoots, load
         if (a->m+a->t > a->b+a->p) return load();
         // Otherwise use random strategy
         if (GetRandomInteger(2)) return load();
         if (GetRandomInteger(1)) {
            if (a->b > a->p) return metal();
            return thermal();
         }
         if (a->m > a->t) return plasma();
         if (a->t > a->m) return bullet();
         return (GetRandomInteger(1))?bullet():plasma();
      case 033:
         {
            // At full 3 on 3, apply random strategy
            // weighted by opponent's histogram of this state...
            // (the extra 1 weights towards plasma)
            unsigned sr=
               GetRandomInteger
               (a->l+a->t+a->p+a->b+a->m+1);
            // Shoot bullets proportional to how much
            // opponent loads or defends using thermal
            if (sr < a->l+a->t) return bullet();
            sr-=(a->l+a->t);
            // Defend with thermal proportional to how
            // much opponent attacks with plasma (tending to
            // waste his ammo)
            if (sr < a->p) return thermal();
            // Shoot plasma proportional to how
            // much opponent shoots bullets or raises metal
            return plasma();
         }
      }
      // Should never hit this; but rather than ruin everyone's fun,
      // if we do, we just load
      return load();
   }
   // Complete override; we use our opponent's model, not history.
   void perceive(Player::Action action) {
      // We want the ammo but not the history; since
      // the framework (Player::perceive) is "all or nothing", 
      // StudiousPlayer just tracks the ammo itself
      switch (action) {
      default: break;
      case Player::LOAD:   ++ammoOpponent; break;
      case Player::BULLET: --ammoOpponent; break;
      case Player::PLASMA: ammoOpponent-=2; break;
      }
      // Now we get the opponent's action based
      // on the last (incoming) ammo state
      OpponentAction* a = actn(osa);
      // ...if it's null just bail
      if (!a) return;
      // Otherwise, count the action
      switch (action) {
      case Player::LOAD    : ++a->l; break;
      case Player::BULLET  : ++a->b; break;
      case Player::PLASMA  : ++a->p; break;
      case Player::METAL   : ++a->m; break;
      case Player::THERMAL : ++a->t; break;
      }
   }
private:
   Opponent& strat;
   OpponentAction* actn(unsigned octalOsa) {
      unsigned ndx = (octalOsa%4)+4*(octalOsa/8);
      if (ndx==0) return 0;
      --ndx;
      if (ndx<15) return &strat.os[ndx];
      return 0;
   }
   unsigned osa;
   unsigned ammoOpponent;
   // Welcome, non-C++ persons, to the "Meyers style singleton".
   // "theMap" is initialized (constructed; initially empty)
   // the first time the declaration is executed.
   static std::unordered_map<size_t, Opponent>& storedLs() {
      static std::unordered_map<size_t, Opponent> theMap;
      return theMap;
   }
};

#endif

请注意，这会根据挑战规则来跟踪有关对手的信息；请参阅底部的范围为“ storedLs（）”的“迈耶风格单例”。（有些人想知道如何执行此操作；现在您知道了！）

的 GunClubPlayer

从原始问题中删除。这用作派生播放器的简约实现的示例。该玩家将参加比赛。

他们GunClubPlayer喜欢去枪支俱乐部。在每个决斗期间，他们将首先加载弹药，然后发射子弹，然后重复此过程直到世界决斗结束。他们实际上并不关心自己是否获胜，而只专注于自己拥有愉快的经历。

// GunClubPlayer.hpp
// A gun club enthusiast. Minimalistic example of derived class

#ifndef __GUN_CLUB_PLAYER_HPP__
#define __GUN_CLUB_PLAYER_HPP__

#include "Player.hpp"

class GunClubPlayer final: public Player
{
public:
    GunClubPlayer(size_t opponent = -1) : Player(opponent) {}

public:
    virtual Action fight()
    {
        return getTurn() % 2 ? bullet() : load();
    }
};

#endif // !__GUN_CLUB_PLAYER_HPP__

的 PlasmaPlayer

等离子播放器喜欢发射等离子螺栓。他将尝试尽可能多地装载和射击。但是，当对手拥有等离子弹药时，他将使用自己的隔热罩（子弹用于弱弹）。

#ifndef __PLASMA_PLAYER_HPP__
#define __PLASMA_PLAYER_HPP__

#include "Player.hpp"

class PlasmaPlayer final : public Player
{
public:
    PlasmaPlayer(size_t opponent = -1) : Player(opponent) {}

    virtual Action fight()
    {
        // Imma Firin Mah Lazer!
        if (getAmmo() > 1) return plasma();

        // Imma Block Yur Lazer!
        if (getAmmoOpponent() > 1) return thermal();

        // Imma need more Lazer ammo
        return load();
    }
};

#endif // !__PLASMA_PLAYER_HPP__

很 SadisticShooter

他宁愿看着你受苦，也不愿杀死你。他并不傻，会按要求遮掩自己。

如果您完全无聊且可以预测，他会直接杀死您。

// SadisticShooter by muddyfish
// PPCG: http://codegolf.stackexchange.com/a/104947/11933

// SadisticShooter.hpp
// A very sad person. He likes to shoot people.

#ifndef __SAD_SHOOTER_PLAYER_HPP__
#define __SAD_SHOOTER_PLAYER_HPP__

#include <cstdlib>
#include "Player.hpp"
// #include <iostream>

class SadisticShooter final : public Player
{
public:
    SadisticShooter(size_t opponent = -1) : Player(opponent) {}
private:
    bool historySame(std::vector<Action> const &history, int elements) {
        if (history.size() < elements) return false;

        std::vector<Action> lastElements(history.end() - elements, history.end());

        for (Action const &action : lastElements)
            if (action != lastElements[0]) return false;
        return true;
    }
public:
    virtual Action fight()
    {
        int my_ammo = getAmmo();
        int opponent_ammo = getAmmoOpponent();
        int turn_number = getTurn();
        //std::cout << " :: Turn " << turn_number << " ammo: " << my_ammo << " oppo: " << opponent_ammo << std::endl;

        if (turn_number == 90) {
            // Getting impatient
            return load();
        }
        if (my_ammo == 0 && opponent_ammo == 0) {
            // It would be idiotic not to load here
            return load();
        }
        if (my_ammo >= 2 && historySame(getHistoryOpponent(), 3)) {
            if (getHistoryOpponent()[turn_number - 1] == THERMAL) return bullet();
            if (getHistoryOpponent()[turn_number - 1] == METAL) return thermal();
        }
        if (my_ammo < 2 && opponent_ammo == 1) {
            // I'd rather not die thank you very much
            return metal();
        }
        if (my_ammo == 1) {
            if (opponent_ammo == 0) {
                // You think I would just shoot you?
                return load();
            }
            if (turn_number == 2) {
                return thermal();
            }
            return bullet();
        }
        if (opponent_ammo >= 2) {
            // Your plasma weapon doesn't scare me
            return thermal();
        }
        if (my_ammo >= 2) {
            // 85% more bullet per bullet
            if (turn_number == 4) return bullet();
            return plasma();
        }
        // Just load the gun already
        return load();
    }
};

#endif // !__SAD_SHOOTER_PLAYER_HPP__

的 TurtlePlayer

TurtlePlayer是个胆小鬼。他大部分时间都躲在盾牌后面-因此得名。有时，他可能会从炮弹中出来（没有双关语）并开枪，但通常他会在敌人弹药的时候低处躺下。

这个机器人不是特别出色-但是，每个KOTH都需要一些初始条目才能使其运行:)

本地测试发现，这赢得对阵双方GunClubPlayer和Opportunist时间的100％。BotRobotPlayer由于双方都躲在盾牌后面，因此与之作战似乎总是会导致平局。

#include "Player.hpp"

// For randomness:
#include <ctime>
#include <cstdlib>

class TurtlePlayer final : public Player {

public:
    TurtlePlayer(size_t opponent = -1) : Player(opponent) { srand(time(0)); }

public:
    virtual Action fight() {
        if (getAmmoOpponent() > 0) {
            // Beware! Opponent has ammo!

            if (rand() % 5 == 0 && getAmmo() > 0) 
                // YOLO it:
                return getAmmo() > 1 ? plasma() : bullet();

            // Play it safe:
            if (getAmmoOpponent() == 1) return metal();
            return rand() % 2 ? metal() : thermal();
        }

        if (getAmmo() == 0) 
            // Nobody has ammo: Time to load up.
            return load();

        else if (getAmmo() > 1) 
            // We have enough ammo for a plasma: fire it!
            return plasma();

        else 
            // Either load, or take a shot.
            return rand() % 2 ? load() : bullet();
    }
};