Question

我知道这可能是一个非常愚蠢的问题，但是到底是什么......

我目前正在尝试实现使用boltzmann分布的soft max动作选择器。

Formula

我有点不确定，如果你想使用特定的动作怎么知道怎么知道？我的意思是这个函数为我提供了概率？，但是如何使用它来选择我想要执行的操作呢？

Answer 1

对于某些机器学习应用程序，有一点需要将一组原始输出（如来自神经网络）映射到一组概率，归一化为总和为1.

在加强学习中，有一套可用的行动＆＃39;权重可能需要映射到一组相关概率，然后用于随机选择下一步采取的行动。

Softmax函数通常用于将输出权重映射到一组相应的概率。 A＆＃34;温度＆＃34;参数允许调整选择策略，在纯粹的利用（一个＆＃34;贪婪＆＃34;策略，总是选择最高权重的行为）和纯粹的探索（每个行为具有相同的被选择概率）之间进行插值。

这是使用Softmax功能的简单示例。每个＆＃34;动作＆＃34;对应于此代码中传递的vector<double>个对象中的一个索引条目。

#include <iostream>
#include <iomanip>
#include <vector>
#include <random>
#include <cmath>


using std::vector;

// The temperature parameter here might be 1/temperature seen elsewhere.
// Here, lower temperatures move the highest-weighted output
// toward a probability of 1.0.
// And higer temperatures tend to even out all the probabilities,
// toward 1/<entry count>.
// temperature's range is between 0 and +Infinity (excluding these
// two extremes).
vector<double> Softmax(const vector<double>& weights, double temperature) {
    vector<double> probs;
    double sum = 0;
    for(auto weight : weights) {
        double pr = std::exp(weight/temperature);
        sum += pr;
        probs.push_back(pr);
    }
    for(auto& pr : probs) {
        pr /= sum;
    }
    return probs;
}

// Rng class encapsulates random number generation
// of double values uniformly distributed between 0 and 1,
// in case you need to replace std's <random> with something else.
struct Rng {
    std::mt19937 engine;
    std::uniform_real_distribution<double> distribution;
    Rng() : distribution(0,1) {
        std::random_device rd;
        engine.seed(rd());
    }
    double operator ()() {
        return distribution(engine);
    }
};

// Selects one index out of a vector of probabilities, "probs"
// The sum of all elements in "probs" must be 1.
vector<double>::size_type StochasticSelection(const vector<double>& probs) {

    // The unit interval is divided into sub-intervals, one for each
    // entry in "probs".  Each sub-interval's size is proportional
    // to its corresponding probability.

    // You can imagine a roulette wheel divided into differently-sized
    // slots for each entry.  An entry's slot size is proportional to
    // its probability and all the entries' slots combine to fill
    // the entire roulette wheel.

    // The roulette "ball"'s final location on the wheel is determined
    // by generating a (pseudo)random value between 0 and 1.
    // Then a linear search finds the entry whose sub-interval contains
    // this value.  Finally, the selected entry's index is returned.

    static Rng rng;
    const double point = rng();
    double cur_cutoff = 0;

    for(vector<double>::size_type i=0; i<probs.size()-1; ++i) {
        cur_cutoff += probs[i];
        if(point < cur_cutoff) return i;
    }
    return probs.size()-1;
}

void DumpSelections(const vector<double>& probs, int sample_count) {
    for(int i=0; i<sample_count; ++i) {
        auto selection = StochasticSelection(probs);
        std::cout << " " << selection;
    }
    std::cout << '\n';
}

void DumpDist(const vector<double>& probs) {
    auto flags = std::cout.flags();
    std::cout.precision(2);
    for(vector<double>::size_type i=0; i<probs.size(); ++i) {
        if(i) std::cout << "  ";
        std::cout << std::setw(2) << i << ':' << std::setw(8) << probs[i];
    }
    std::cout.flags(flags);
    std::cout << '\n';
}

int main() {
    vector<double> weights = {1.0, 2, 6, -2.5, 0};

    std::cout << "Original weights:\n";
    for(vector<double>::size_type i=0; i<weights.size(); ++i) {
        std::cout << "    " << i << ':' << weights[i];
    }
    std::cout << "\n\nSoftmax mappings for different temperatures:\n";
    auto softmax_thalf  = Softmax(weights, 0.5);
    auto softmax_t1     = Softmax(weights, 1);
    auto softmax_t2     = Softmax(weights, 2);
    auto softmax_t10    = Softmax(weights, 10);

    std::cout << "[Temp 1/2] ";
    DumpDist(softmax_thalf);
    std::cout << "[Temp 1]   ";
    DumpDist(softmax_t1);
    std::cout << "[Temp 2]   ";
    DumpDist(softmax_t2);
    std::cout << "[Temp 10]  ";
    DumpDist(softmax_t10);

    std::cout << "\nSelections from softmax_t1:\n";
    DumpSelections(softmax_t1, 20);
    std::cout << "\nSelections from softmax_t2:\n";
    DumpSelections(softmax_t2, 20);
    std::cout << "\nSelections from softmax_t10:\n";
    DumpSelections(softmax_t10, 20);
}

以下是输出示例：

Original weights:
    0:1    1:2    2:6    3:-2.5    4:0

Softmax mappings for different temperatures:
[Temp 1/2]  0: 4.5e-05   1: 0.00034   2:       1   3: 4.1e-08   4: 6.1e-06
[Temp 1]    0:  0.0066   1:   0.018   2:    0.97   3:  0.0002   4:  0.0024
[Temp 2]    0:   0.064   1:    0.11   2:    0.78   3:   0.011   4:   0.039
[Temp 10]   0:    0.19   1:    0.21   2:    0.31   3:    0.13   4:    0.17

Selections from softmax_t1:
 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1

Selections from softmax_t2:
 2 2 2 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 2 1

Selections from softmax_t10:
 0 0 4 1 2 2 2 0 0 1 3 4 2 2 4 3 2 1 0 1

使用softmax进行动作选择？

1 个答案: