Non-Greedy and Greedy Quantifier In Perl

本文详细对比了正则匹配中的贪心与非贪心方式,通过实例展示了不同匹配策略在实际应用中的效果,并提供了解决特定需求的代码示例。
比较以下两个正则匹配:
$_ = "fred and barney went bowling last night ";

1)贪心
/fred.+barney/
过程:首先匹配成功fred,然后.+匹配之后尽量多的信息,即:and barney went bowling last night,随后barney匹配不成功,于是.+往回到字母t,再次匹配barney,还是不成功,继续.+往回直到最后barney匹配成功。

2)非贪心
/fred.+?barney/ 
过程:首先匹配成功fred,然后.+匹配之后尽量少的信息,即:空格,随后barnay匹配不成功,于是.+往前倒a,再匹配barney,还是不成功,继续 .+往前直到最后barney匹配成功。

可以发现:如果fred和barney比较近的时候,用贪心方式效率更好;如果fred和barney分别再两端的时候,用非贪心方式效率更好。

又如:$_ = "I thought you said Fred and Velma, not  Wilma"
如果要删除的修饰符,则
1)s#.+#$1#g 
得到 I thought you said Fred and Velma, not  Wilma

2)s#.+?#$1#g
 得到 I thought you said Fred and Velma, not  Wilma

来自 “ ITPUB博客 ” ,链接:http://blog.itpub.net/24104518/viewspace-721956/,如需转载,请注明出处,否则将追究法律责任。

转载于:http://blog.itpub.net/24104518/viewspace-721956/

### 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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