Gym - 101061A 思维题

本文介绍了一个有趣的数学问题:通过特定规则放置数字到两个盒子中,并利用二进制来判断数字最终会落入哪个盒子。文章提供了一种简单有效的方法来确定任意给定数字的最终归属。

给两个盒子 进行如下操作循环
选择一个没有被放入盒子的最小的数 X 放入第一个盒子
将2*X放入第二个盒子。

╮(╯▽╰)╭ 看到2*x这种东西就应该往二进制上想。。然而我没想到。。
首先 奇数肯定都在第一个盒子里, 奇数的二进制的最后一位是1
奇数的两倍肯定在第二个盒子里 这些数的二进制末尾0的的数量是1
所以每一个末尾0的数量为1的偶数都在第二个盒子里
那么每一个末尾0的数量为2的偶数都在第一个盒子里
那么每一个末尾0的数量为3的偶数都在第二个盒子里
。。。。。。。
答案就这么出来了。

#include <iostream>
#include <cstdio>
#include <algorithm>
#include <cstring>
#include <vector>
#define MAX 1007
#define mem(a,b) memset(a,b,sizeof a);
using namespace std;
typedef long long LL;
const LL INF=(1LL<<60);
vector<int> e[MAX];
int main()
{
    int n;
    scanf("%d",&n);
    while(n--)
    {
        long long u;
        cin>>u;
        if(u&1)
        {
            puts("First Box");
        }
        else
        {
            int ans=0;
            while(u%2==0)
            {
                ans++;
                u/=2;
            }
            if(ans&1)
                puts("Second Box");
            else
                puts("First Box");
        }
    }
}
### Gym-Gazebo Library for Robotics Simulation and Reinforcement Learning The **gym-gazebo** library is designed as a comprehensive toolset for roboticists, integrating simulation environments, middleware systems like ROS or ROS 2, and advanced machine learning paradigms such as reinforcement learning into one cohesive framework[^1]. This combination allows researchers and developers to design, test, and refine behavioral algorithms for robots within simulated conditions before deploying them onto real-world hardware. #### Key Features of gym-gazebo - It leverages the power of Gazebo—a widely-used physics simulator—to provide realistic simulations of robot dynamics. - The integration with ROS/ROS 2 enables seamless communication between different components involved in both simulation and physical deployment scenarios. - By incorporating reinforcement learning methodologies, it facilitates training models through trial-and-error processes while optimizing performance metrics over time. An updated version called **Gym-Ignition**, introduced later by some contributors working along similar lines but using Ignition instead of classic GAZEBOSimulations offers reproducible experiments which are crucial when comparing results across studies involving deep reinforcement learning applications specifically tailored towards autonomous vehicles among other domains mentioned earlier under references four & five respectively.[^4] Here’s how you can install `gym-gazebo` via pip command typically used inside Python virtual environments: ```bash pip install gym-gazebo ``` For more customized installations depending upon specific versions required alongside compatible dependencies including particular releases associated either directly from source repositories hosted generally at GitHub pages maintained actively throughout recent years until now; refer official documentation links provided usually after each release announcement posts found easily searching terms related above citations given previously too!
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