Gym - 101142A

本文介绍了一种解决简单几何问题的方法,通过判断输入坐标的位置关系,输出特定的几何形状边界点坐标。使用C++实现,适合初学者理解基本的条件判断和输出格式。

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团队赛的第一次比赛,这次用的是国外的题,也是我第一次的总结的题,这是这次比赛的第一个比赛的题很水,也是很考验脑子的思路的,

因为是输出任意情况中的一种那么就非常简单了,只要寻找每个点所对应的边界上的点就可以了,看下代码就很容易懂了。

#include<algorithm>
#include<cstdio>
#include<cstring>
#include<queue>
#include<stack>
#include<set>
#include<map>
#include<iostream>
#include<string>
using namespace std;
int main()
{
    int w, h, x1, y1, x2, y2;
    freopen("anniversary.in", "r", stdin);
    freopen("anniversary.out", "w", stdout);
    while(~scanf("%d %d %d %d %d %d",&w, &h, &x1, &y1, &x2, &y2))
    {
        if(x1 == x2 && y1 != y2)
        {
            printf("0 %d %d %d\n",y1, w, y2);
        }
        if(y1 == y2 && x1 != x2)
        {
            printf("%d 0 %d %d\n",x1, x2, h);
        }
        if(x1 != x2 && y1 != y2)
        {
            printf("%d 0 %d %d\n", x1, x2, h);
        }
    }
    return 0;


}


### 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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