所以有alpha_beta_two_steps()就可以实现两步决策了,不需要alpha_beta_next()了吧
时间: 2023-08-18 14:04:06 浏览: 53
实际上,alpha_beta_two_steps()函数是在alpha_beta_next()函数的基础上进行了扩展,实现了两步决策。两个函数的主要区别在于,alpha_beta_next()只考虑了下一步的决策,而alpha_beta_two_steps()在下一步决策的基础上再进行了一步决策。所以如果你只需要实现单步决策,那么可以直接使用alpha_beta_next()函数;如果需要实现两步决策,那么可以使用alpha_beta_two_steps()函数。但是两个函数的核心算法都是Alpha-Beta剪枝算法,只是搜索深度和搜索的决策层数不同。
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Alpha_beta剪枝井字棋C++代码
以下是一个简单的Alpha_beta剪枝井字棋C代码:
#include <stdio.h>
#define N 3
#define human 'O'
#define computer 'X'
#define empty ' '
int board[N][N];
void print_board() {
int r, c;
printf("\n");
for (r = 0; r < N; r++) {
for (c = 0; c < N; c++) {
printf("%c", board[r][c]);
if (c != N - 1)
printf("|");
}
printf("\n");
if (r != N - 1) {
for (c = 0; c < N; c++)
printf("--");
printf("\n");
}
}
}
int evaluate(char player) {
int r, c;
int score = 0;
char opponent = (player == human) ? computer : human;
// check rows
for (r = 0; r < N; r++) {
int player_count = 0, opponent_count = 0;
for (c = 0; c < N; c++) {
if (board[r][c] == player)
player_count++;
else if (board[r][c] == opponent)
opponent_count++;
}
if (player_count == N)
return 1000;
if (opponent_count == N)
return -1000;
score += player_count * player_count;
score -= opponent_count * opponent_count;
}
// check columns
for (c = 0; c < N; c++) {
int player_count = 0, opponent_count = 0;
for (r = 0; r < N; r++) {
if (board[r][c] == player)
player_count++;
else if (board[r][c] == opponent)
opponent_count++;
}
if (player_count == N)
return 1000;
if (opponent_count == N)
return -1000;
score += player_count * player_count;
score -= opponent_count * opponent_count;
}
// check diagonals
int player_count = 0, opponent_count = 0;
for (r = 0; r < N; r++) {
if (board[r][r] == player)
player_count++;
else if (board[r][r] == opponent)
opponent_count++;
}
if (player_count == N)
return 1000;
if (opponent_count == N)
return -1000;
score += player_count * player_count;
score -= opponent_count * opponent_count;
player_count = 0, opponent_count = 0;
for (r = 0; r < N; r++) {
if (board[r][N - r - 1] == player)
player_count++;
else if (board[r][N - r - 1] == opponent)
opponent_count++;
}
if (player_count == N)
return 1000;
if (opponent_count == N)
return -1000;
score += player_count * player_count;
score -= opponent_count * opponent_count;
return score;
}
int alphabeta(int depth, int alpha, int beta, char player) {
int r, c, score;
if (depth == 0)
return evaluate(player);
char opponent = (player == human) ? computer : human;
// maximize score if it's computer's turn
if (player == computer) {
score = alpha;
for (r = 0; r < N; r++) {
for (c = 0; c < N; c++) {
if (board[r][c] == empty) {
board[r][c] = computer;
score = alphabeta(depth - 1, alpha, beta, opponent);
board[r][c] = empty;
if (score > alpha)
alpha = score;
if (beta <= alpha)
return alpha;
}
}
}
return alpha;
}
// minimize score if it's human's turn
else {
score = beta;
for (r = 0; r < N; r++) {
for (c = 0; c < N; c++) {
if (board[r][c] == empty) {
board[r][c] = human;
score = alphabeta(depth - 1, alpha, beta, opponent);
board[r][c] = empty;
if (score < beta)
beta = score;
if (beta <= alpha)
return beta;
}
}
}
return beta;
}
}
void computer_move() {
int r, c;
int max_score = -1000;
char opponent = human;
for (r = 0; r < N; r++) {
for (c = 0; c < N; c++) {
if (board[r][c] == empty) {
board[r][c] = computer;
int score = alphabeta(4, -1000, 1000, opponent);
board[r][c] = empty;
if (score > max_score) {
max_score = score;
printf("Computer moves to (%d, %d) with score %d\n", r, c, score);
}
}
}
}
}
int main() {
int r, c;
for (r = 0; r < N; r++) {
for (c = 0; c < N; c++) {
board[r][c] = empty;
}
}
printf("Let's play Tic Tac Toe!\n");
print_board();
while (1) {
printf("\nYour move:\n");
scanf("%d%d", &r, &c);
if (r < 0 || r >= N || c < 0 || c >= N || board[r][c] != empty) {
printf("Invalid move. Try again.\n");
continue;
}
board[r][c] = human;
print_board();
int score = evaluate(human);
if (score == 1000) {
printf("You win!\n");
break;
}
else if (score == -1000) {
printf("Computer wins!\n");
break;
}
else if (score == 0) {
printf("Tie game!\n");
break;
}
printf("\nComputer's move:\n");
computer_move();
print_board();
score = evaluate(computer);
if (score == 1000) {
printf("Computer wins!\n");
break;
}
else if (score == -1000) {
printf("You win!\n");
break;
}
else if (score == 0) {
printf("Tie game!\n");
break;
}
}
return 0;
}
function [Lnum_max, num] = p2p(Alpha, Beta)
函数 `p2p(Alpha, Beta)` 的描述看起来像是某种概率论或统计学中的函数,特别是在处理泊松分布(Poisson distribution)相关的计算。在这个上下文中,`Alpha` 和 `Beta` 可能是泊松分布的两个参数,`Lambda` 或均值(平均发生次数),`Alpha` 可能代表 Lambda 的估计值,而 `Beta` 可能是一个超参数或者置信水平等。
`Lnum_max` 可能是最大似然估计的最大观测数(即最大的累积概率对应的数值),`num` 可能是用于计算某个概率阈值下的预期观察次数。
具体来说,这个函数可能是计算给定 `Alpha` 和 `Beta` 下,泊松分布中达到某个概率阈值(比如0.95)所需要的最小观察次数(`Lnum_max`)以及相应的期望总次数(`num`)。例如:
```matlab
function [Lnum_max, num] = p2p(Alpha, Beta)
% 计算泊松分布的累积概率
CDF = poisscdf(Alpha, Beta); % Alpha相当于lambda
% 找到累积概率等于或大于0.95的那个点
threshold_index = find(CDF >= 0.95, 1);
if ~isempty(threshold_index)
Lnum_max = threshold_index;
num = Alpha * (threshold_index + 1); % 期望次数等于 lambda*(索引+1)
else
% 如果找不到满足条件的概率,可能返回错误或设置默认值
Lnum_max = Inf; % 或者抛出异常
num = NaN; % 表示无法确定
end
end
```