Double it Gym-101466D

本文介绍了一种通过逆向思维解决从0到任意整数n的转换问题的方法,利用两种转换规则A(y=2*x+1)和B(y=2*x+2),从目标值n出发逆向回溯至初始状态0,并记录每一步的操作。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

Double it Gym-101466D


题目链接

题意:
给出一个n,问怎样得到n的。(1 <= n <= 10^9)
我们开始有0块钱,可以通过下面两个方法获得更多的钱:
A:y = 2 * x + 1
B:y = 2 * x + 2

做法:
我们逆向考虑一下,题目问我们怎么从0->n,我们从n->0倒过来推。
于是有:
A:x = (y - 1) / 2
B:x = (y - 2) / 2

因为从0->n获得钱的过程中,不会出现钱不是整数的情况,
所以我们从n->0倒过来推,同样如此。
同时,我们发现A和B中,是不会同时可以整除的,也就是说A和B不会同时成立。
因为
A:减1再除以2
B:减2再除以2
(我突然想到了奇偶数,一个数不会既是奇数又是偶数)
所以,我们只需将n整除(通过A/B)至0,同时记录A/B,最后倒过来输出就ok了。

#include <stdio.h>

char ans[1000005];

int
main() {
    int n, i, t = 0;

    scanf("%d", &n);
    while( n!= 0 ) {
        if( (n - 1) % 2 == 0 ) {
            n = (n - 1) / 2;
            ans[t] = 'A';
            t++;
        }
        else if( (n - 2) % 2 == 0 ) {
            n = (n - 2) / 2;
            ans[t] = 'B';
            t++;
        }
    }
    for( i = t - 1; i >= 0; i-- ) {
        printf("%c", ans[i]);
    }
    printf("\n");

    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!
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值