Gym - 101666I

本文解析了一款切巧克力游戏背后的算法逻辑,通过分析不同情况下玩家的最优策略,揭示了贪心算法在解决特定问题时的应用。特别是当巧克力的长宽比为奇偶混合时,如何通过策略选择实现利益最大化。

规律题,贪心题

你站在左边切巧克力,你妹妹站在下方切巧克力,你们都希望黑的更多,其他情况都很显然,只有p%2==1 && q%2==0的时候比较特殊,因为这个时候你可以吧右上方的白块给你妹妹,你妹妹也可以给你,于是看你们怎么取,你在取得奇数列优势为1的时候,你妹妹和你都会选择保守选择2列,2行使得不亏不赚,最后那个拿到右上方白巧克力的一定吃亏。

#include<cstdio>
#include<cstring>
#define maxl 110

int p,q;
int ans;

int main()
{
	while(~scanf("%d%d",&p,&q))
	{
		if(p==1 && q==1)
			ans=1;
		else
		if(p==1)
		{
			if(q&1)
				ans=1;
			else
				ans=2;
		}
		else
		if(q==1)
		{
			if(p&1)
				ans=1;
			else
				ans=0;
		}
		else
		{
			if(p%2==0 && q%2==1)
				ans=0;
			else
			if(p%2==1 && q%2==0)
			{
				if(p>q)
					ans=0;
				else
					ans=2;
			}
			else
			if(p%2==0 && q%2==0)
				ans=0;
			else
			if(p%2==1 && q%2==1)
				ans=1;
		}
		printf("%d\n",ans);
	}
	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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