Greedy

1789 Doing Homework again 经典的贪心算法问题(任务调度问题),具体的贪心策略:首先对各项任务进行排序(按score的递减顺序、deadline的递增顺序),然后对每一项任务,先从其deadline开始,按照时间的递减顺序考虑尚未填入任务号的时间空位(初始化1到n的所有时间空位都为空的),如果存在这样的空位,则将任务填入最近的空位,否则将任务放在一个时间最远的空位上,以下为本题的具体代码

1074 Doing Homework :注意区别这一题和上面一题,这题就不能简单地用贪心来解决了。AndreMouche大牛的博客上有具体地用状态压缩DP的方法来解决这题

 

3177 Crixalis's Equipment :这题的贪心策略比较难想到:为了先处理搬动空间较大但摆放空间较小的家具,也就是按照搬动和摆放家具所需空间差值的递减顺序进行判断,这样一来,代码就比较容易敲出来

### 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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