Web2. `arr = np.random.rand(10,5)`: This creates a NumPy array with 10 rows and 5 columns, where each element is a random number between 0 and 1. The `rand()` function in NumPy generates random values from a uniform distribution over [0, 1). So, the final output of this code will be a 10x5 NumPy array filled with random numbers between 0 and 1. Webif np.random.uniform() < self.epsilon:#np.random.uniform生成均匀分布的随机数，默认0-1，大概率选择actions_value最大下的动作 # forward feed the observation and get q …
Expected SARSA in Reinforcement Learning - GeeksforGeeks
Web为什么需要DQN我们知道，最原始的Q-learning算法在执行过程中始终需要一个Q表进行记录，当维数不高时Q表尚可满足需求，但当遇到指数级别的维数时，Q表的效率就显得十分有限。因此，我们考虑一种值函数近似的方法，实现每次只需事先知晓S或者A，就可以实时得到其对应的Q值。 Webif np.random.uniform () < self.epsilon: # forward feed the observation and get q value for every actions actions_value = self.critic.forward (observation) action = np.argmax (actions_value) else: action = np.random.randint (0,2) # 0,1 随机抽 return action def learn (self): for episode in range (self.episodes): state = self.env.reset () done = False protein s antigen free
Q-Learning实现（FrozenLake-v0） - 知乎 - 知乎专栏
WebQ-Learning算法的伪代码如下：. 环境使用gym中的FrozenLake-v0，它的形状为：. import gym import time import numpy as np class QLearning(object): def __init__(self, … Web14 apr. 2024 · self.memory_counter = 0 transition = np.hstack((s, [a,r], s_)) # replace the old memory with new memory index = self.memory_counter % self.memory_size self.memory.iloc[index, :] = transition self.memory_counter += 1 def choose_action(self, observation): observation = observation[np.newaxis, :] if np.random.uniform() … Web2. `arr = np.random.rand(10,5)`: This creates a NumPy array with 10 rows and 5 columns, where each element is a random number between 0 and 1. The `rand()` function in … protein s and protein c in clotting disorder