UVa 119 - Greedy Gift Givers

本文深入探讨了信息技术领域的多个细分技术领域,包括前端开发、后端开发、移动开发、游戏开发等,提供了关于大数据开发、开发工具、嵌入式硬件、音视频基础、AI音视频处理等领域的详细分析。

题目:有n个人,每个人分别拿出一些钱给其中的某些人买东西,问最后每个人的赢亏。

分析:简单题,模拟。直接模拟即可,注意如果不能够平分,就按整除计算,余数留下来。

说明:UVa终于冲进前2000了╮(╯▽╰)╭。

#include <iostream>
#include <cstdlib>

using namespace std;

char name[11][15],temp[11];
int  cost[11];

int ID( int n, char str[] )
{
	for ( int j,i = 0 ; i < n ; ++ i ) {
		for ( j = 0 ; str[j] == name[i][j] ; ++ j )
			if ( !str[j] ) break;
		if ( !str[j] ) return i;
	}
	return -1;
}

int main()
{
	int n,m,money,out,in,tests = 0;
	while ( cin >> n ) {
		if ( tests ++ ) cout << endl;
		
		for ( int i = 0 ; i < n ; ++ i ) {
			cin >> name[i];
			cost[i] = 0;
		}
		
		for ( int i = 0 ; i < n ; ++ i ) {
			cin >> temp;
			out = ID( n, temp );
			cin >> money >> m;
			for ( int j = 0 ; j < m ; ++ j ) {
				cin >> temp;
				in = ID( n, temp );
				cost[out] -= money/m;
				cost[in]  += money/m;
			}
		}
		
		for ( int i = 0 ; i < n ; ++ i )
			cout << name[i] << " " << cost[i] << endl;
	}
	return 0;
}

### Epsilon-Greedy Algorithm Implementation and Use Cases The epsilon-greedy algorithm is a strategy commonly used in reinforcement learning to balance exploration and exploitation. In this context, exploration refers to trying out new actions to discover potentially better outcomes, while exploitation involves selecting the action that has historically provided the best reward. #### Algorithm Implementation The epsilon-greedy policy selects a random action with probability ε (epsilon) and the greedy action (the one with the highest estimated value) with probability 1 - ε. This ensures that the agent does not always exploit known information but also explores other options to avoid getting stuck in suboptimal strategies[^2]. Below is an implementation of the epsilon-greedy algorithm in Python: ```python import numpy as np def epsilon_greedy_policy(Q, state, epsilon): if np.random.rand() < epsilon: # Exploration: Select a random action return np.random.choice(len(Q[state])) else: # Exploitation: Select the action with the highest value return np.argmax(Q[state]) ``` In this code snippet, `Q` represents the action-value function estimate for each state-action pair, `state` is the current state, and `epsilon` determines the likelihood of choosing a random action over the optimal one. #### Use Cases Epsilon-greedy algorithms are widely applied in various domains where decision-making under uncertainty is required. Some prominent use cases include: 1. **Reinforcement Learning**: The algorithm is fundamental in training agents to solve Markov Decision Processes (MDPs). For instance, it can be employed in games like chess or Go, where the agent must decide between exploring new moves or exploiting known winning strategies[^1]. 2. **Multi-Armed Bandit Problems**: These problems involve maximizing rewards by selecting among multiple options (or "arms") with unknown payoff distributions. Epsilon-greedy policies help determine which arm to pull next by balancing exploration and exploitation. 3. **Recommendation Systems**: In online recommendation systems, such as those used by streaming platforms or e-commerce websites, epsilon-greedy algorithms can suggest items to users. By occasionally recommending less popular items, the system can discover new preferences while primarily offering top-rated suggestions[^3]. 4. **Autonomous Driving**: Self-driving cars use reinforcement learning techniques to navigate roads safely. An epsilon-greedy approach might allow the vehicle to experiment with different driving styles during testing phases before settling on optimal behaviors[^4]. 5. **Resource Allocation**: In cloud computing environments, epsilon-greedy methods can optimize server allocation by dynamically adjusting resources based on historical performance metrics while exploring alternative configurations[^3].
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