帮我写一份演化博弈的MATLAB代码
时间: 2023-02-21 07:43:27 浏览: 155
好的,下面是一份简单的演化博弈的 MATLAB 代码:
```
% 定义每一步操作的获益
T = 1;
R = 3;
P = 0;
S = 5;
% 定义每个人的初始策略
strategy = [0.5, 0.5];
% 定义博弈的次数
n = 1000;
% 开始博弈
for i = 1:n
% 随机决策
decision = rand(1, 2) < strategy;
% 定义每种决策的获益
payout = zeros(1, 2);
payout(decision == [1, 1]) = R;
payout(decision == [1, 0]) = S;
payout(decision == [0, 1]) = T;
payout(decision == [0, 0]) = P;
% 计算策略的改变
strategy = strategy + (rand(1, 2) - 0.5) * 0.1 .* (payout - mean(payout)) / std(payout);
% 限制策略的范围在 0 到 1 之间
strategy = max(0, min(1, strategy));
相关问题
演化博弈matlab代码
以下是一个简单的演化博弈的Matlab代码示例:
```matlab
% 定义初始策略
strategyA = [0.2 0.8];
strategyB = [0.6 0.4];
% 定义收益矩阵
payoffA = [3 0; 5 1];
payoffB = [3 5; 0 1];
% 进行演化博弈
for i = 1:100
% 计算双方的收益
payoffA1 = dot(strategyA, payoffA(1,:));
payoffA2 = dot(strategyA, payoffA(2,:));
payoffB1 = dot(strategyB, payoffB(:,1));
payoffB2 = dot(strategyB, payoffB(:,2));
% 计算双方的平均收益
avg_payoffA = (payoffA1 + payoffA2) / 2;
avg_payoffB = (payoffB1 + payoffB2) / 2;
% 根据收益更新策略
if avg_payoffA > avg_payoffB
strategyA = strategyA + randn(size(strategyA)) * 0.01;
else
strategyB = strategyB + randn(size(strategyB)) * 0.01;
end
% 归一化策略
strategyA = strategyA / sum(strategyA);
strategyB = strategyB / sum(strategyB);
end
% 输出最终策略
disp('Final strategy of player A:');
disp(strategyA);
disp('Final strategy of player B:');
disp(strategyB);
```
在这个示例中,我们定义了两个玩家的初始策略和收益矩阵,并进行了100次演化博弈的迭代。在每次迭代中,我们计算了双方的收益和平均收益,并根据平均收益更新策略。最后,输出了双方的最终策略。请注意,这只是一个简单的演化博弈示例,实际应用中需要根据具体情况进行调整。
演化博弈MATLAB代码
您好!演化博弈是一个研究博弈策略演变的领域。如果您需要MATLAB代码来模拟演化博弈,可以参考以下代码:
```
function [strategy, scores] = evol_game(n, T, S, P)
% n: number of players
% T: number of rounds
% S: score matrix (n x n)
% P: initial probability distribution over strategies (n x 1)
% Initialize the strategy and scores
strategy = zeros(n, T);
scores = zeros(n, T);
% Play the game
for t = 1:T
% Calculate the payoffs for each player
for i = 1:n
for j = 1:n
scores(i, t) = scores(i, t) + P(j) * S(i, j);
end
end
% Update the strategy probability distribution
P = P .* exp(scores(:, t) / (n * T));
P = P / sum(P);
% Record the strategy for each player
[~, strategy(:, t)] = max(S, [], 2);
end
end
```
此代码实现了简单的演化博弈模型,其中:
- `n`:玩家的数量
- `T`:回合数
- `S`:得分矩阵(n x n)
- `P`:策略分布的初始概率(n x 1)
输出:
- `strategy`:每个玩家在每回合中采用的策略
- `scores`:每个玩家在每回合中获得的得分
希望这些代码能帮助您。如果您需要更详细的解释,请告诉我。