Gym - 101490B Battle Simulation

本文解析了一个模拟类算法题目,题目要求玩家根据怪兽的不同技能选择相应的奥特曼技能进行反击,若怪兽连续使用三种不同技能,则需使用特殊技能应对。文章通过C语言代码详细展示了如何实现这一逻辑。

题目链接:https://vjudge.net/problem/Gym-101490B
题意:应该就是奥特曼大战怪兽,怪兽有三个技能R,B,L,奥特曼有三个技能,S(克制R),K(克制K),H(克制L),如果怪兽连续出三个不一样的招数,你就必须用大招C来抵抗他,现在给你怪兽的出招表,让你输出奥特曼的出招顺序
解析:直接照着模拟就可以了,对于每一个招数,看一下接下来两个是不是全都不一样,如果都不一样直接输出C,并跳过接下来的两个,否则对应输出即可

#include <bits/stdc++.h>
using namespace std;
const int maxn = 1e6+100;
char a[maxn];
int main(void)
{
    scanf("%s",a);
    int n = strlen(a);
    for(int i=0;i<n;i++)
    {
        if(a[i]!=a[i+1] && a[i]!=a[i+2] && a[i+1]!=a[i+2] && i+1<n && i+2<n)
        {
            printf("C");
            i+=2;
        }
        else
        {
            if(a[i]=='R')
                printf("S");
            else if(a[i]=='B')
                printf("K");
            else if(a[i]=='L')
                printf("H");
        }
    }
    puts("");
    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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