Gym - 101630A

本文介绍了一种通过排序查找的方式实现对放置于水平轴上的圆形目标进行命中判断的方法。利用TreeSet对圆盘坐标进行排序,从而快速定位并判断射击点是否击中圆盘。

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题意

这道题的题意说的是按顺序给出原盘和射击点,原盘坐标是x,y是确定放置在水平轴上的

分析

因为是放在水平轴上,那么在对于一个x轴,相同的x不可能有多个y,并且在一个x轴区间内不会有太多的原盘,那么可以对x轴进行排序查找。

import java.util.Iterator;
import java.util.Scanner;
import java.util.TreeSet;

public class Main {
    static class Target implements Comparable<Target>{
        long  x,y;
        int i;
        public Target(int x, int y, int i) {
            super();
            this.x = x;
            this.y = y;
            this.i = i;
        }
        @Override
        public int compareTo(Target o) {//因为这道题的特殊性质所以在同一个x值下是不可能有两个圆盘同时出现的,这样重载就行了
            if(x<o.x) return -1;
            if(x>o.x) return 1;
            return 0;
        }
        Boolean inside(int x,int y) {
            return (this.x-x)*(this.x-x)+(this.y-y)*(this.y-y)<this.y*this.y;
        }
    }
    public static void main(String[] args) {
        Scanner cin = new Scanner(System.in);
        int n=cin.nextInt();
        TreeSet<Target> set=new TreeSet<>();
        int mr=0;
        for(int i=1;i<=n;i++) {
            int t,x,y;
            t=cin.nextInt();
            x=cin.nextInt();
            y=cin.nextInt();
            if(t==1) {
                set.add(new Target(x, y, i));
                mr=Math.max(mr, y);//最大的半径
            }else {
                //java的treeset迭代器遍历
                Iterator<Target> it = set.subSet(new Target(x-mr, 0, 0), new Target(x+mr, 0, 0)).iterator();//在treeset的这个可能的区间的查找可能命中的原盘,subSet区间左闭右开
                int ans=-1;
                while (it.hasNext()) {
                    Target next=it.next();
                    if(next.inside(x, y)) {
                        ans=next.i;
                        set.remove(next);
                        break;
                    }
                }
                System.out.println(ans);
            }
        }
        cin.close();
    }
}
### 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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