Gym 100735 - I. Yet another A + B

本文介绍了一个基于大数加法的算法题解。题目要求判断三个输入的大数是否可以通过任意两个数相加得到第三个数。通过使用C++结构体实现大数存储和运算,该题解提供了一种有效的方法来解决这一问题。

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题目链接:

http://codeforces.com/gym/100735/problem/I

题意:

给出三个数A,B,C,如果三个数随机排列满足 两个相加等于第三个 的情况输出YES, 否则NO

题解:

大数加法

AC代码 (来自hacb教练)

#include <iostream>
#include <algorithm>
#include <string>
#include <cstring>
#include <stdio.h>
using namespace std;
struct Tr{
    int num[120];
    Tr operator+(Tr a)const
    {
        Tr tm;
        memset(tm.num, 0, sizeof(tm.num));
        int in = 0, i;
        for (i = 0;i < 120;i++)
        {
            tm.num[i] = (num[i]+a.num[i]+in)%10;
            in = (num[i]+a.num[i]+in)/10;
        }
        return tm;
    }
    bool operator==(Tr a)const
    {
        int i;
        for (i = 119;i >= 0;i--)
        {
            if (num[i] != a.num[i]) return false;
        }
        return true;
    }
}A, B, C;

char a[110], b[110], c[110];

int main() {
    int i, j;
    while (~scanf("%s%s%s", a, b, c)) {
        memset(A.num, 0, sizeof(A.num));
        memset(B.num, 0, sizeof(B.num));
        memset(C.num, 0, sizeof(C.num));
        int la = strlen(a), lb = strlen(b), lc = strlen(c);
        for (i = la-1;i >= 0;i--) A.num[la-1-i] = a[i]-'0';
        for (i = lb-1;i >= 0;i--) B.num[lb-1-i] = b[i]-'0';
        for (i = lc-1;i >= 0;i--) C.num[lc-1-i] = c[i]-'0';
        if (A+B == C || A+C == B || B+C==A) puts("YES");
        else if (A+A == C || B+B==C || B+B==A || C+C == B || C+C == A || A+A==B) puts("YES");
        else puts("NO");
    }
}
### AI Pac-Man Search Experiment Project Implementation and Guidance In developing an AI-driven system to play the game of Pac-Man, one can leverage various artificial intelligence techniques including reinforcement learning algorithms which have been shown effective in playing games. For instance, model-based reinforcement learning methods allow agents to learn a model of the environment from experience and then plan within this learned model to make decisions about actions that maximize cumulative reward over time[^2]. To implement such a project, consider structuring it around these components: #### Defining Environment Interaction The first step involves setting up how the agent interacts with the Pac-Man environment. This includes defining states as positions on the grid along with relevant information like ghost locations or power pellets status; actions could be moving up, down, left, right. #### Choosing Learning Algorithm Selecting appropriate machine learning approaches is crucial. Variants of Q-learning or policy gradient methods might serve well here due to their ability to handle sequential decision-making problems under uncertainty effectively. Additionally, incorporating elements from self-supervised learning may help improve generalization by allowing pre-training phases where no explicit rewards are given but useful representations still get formed through interaction alone. #### Implementing Model-Based Reinforcement Learning A key aspect would involve implementing some form of internal world modeling capability so that predictions regarding future events based upon current knowledge become possible. Techniques involving variational autoencoders (VAEs), especially when combined into more complex architectures capable of understanding spatial relationships between objects via graph neural networks, offer promising avenues for achieving robust predictive performance even amidst noisy observations typical during gameplay sessions. ```python import gymnasium as gym from stable_baselines3 import PPO env = gym.make('MsPacman-v0') model = PPO('CnnPolicy', env, verbose=1) def train_model(): model.learn(total_timesteps=int(2e5)) train_model() ``` This code snippet demonstrates initializing a Proximal Policy Optimization algorithm using Stable Baselines 3 library applied against OpenAI Gym's MsPacman environment setup. Training occurs over two hundred thousand timesteps aiming at optimizing policies guiding action selection towards higher scores achieved while navigating mazes filled with ghosts trying to catch our protagonist. --related questions-- 1. What specific challenges does applying deep reinforcement learning pose uniquely within classic arcade games? 2. How do different types of neural network structures impact efficiency versus effectiveness trade-offs seen across multiple runs conducted inside similar environments? 3. Can you provide examples illustrating successful integration points found connecting classical pathfinding strategies alongside modern ML paradigms used today? 4. In what ways has research progressed concerning transferability aspects observed among skills acquired solving one type of problem translating beneficially onto another distinct yet related domain?
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