根据查阅资料，python编写出MAB的 Softmax算法（或Epsilon-Greedy算法），BetaThompson sampling算法，UCB算法以及LinUCB算法。

以下是Python代码实现：

1. Softmax算法：

import numpy as np

def softmax_action_selection(q_values, tau=1.0):
    """
    Softmax action selection algorithm for multi-armed bandit problem.
    :param q_values: numpy array of shape (num_actions,) representing the estimated action values
    :param tau: float temperature parameter controlling the degree of exploration
    :return: selected action
    """
    probabilities = np.exp(q_values / tau) / np.sum(np.exp(q_values / tau))
    action = np.random.choice(len(q_values), p=probabilities)
    return action

2. Epsilon-Greedy算法：

import numpy as np

def epsilon_greedy_action_selection(q_values, epsilon=0.1):
    """
    Epsilon-greedy action selection algorithm for multi-armed bandit problem.
    :param q_values: numpy array of shape (num_actions,) representing the estimated action values
    :param epsilon: float parameter controlling the degree of exploration
    :return: selected action
    """
    if np.random.rand() < epsilon:
        action = np.random.choice(len(q_values))
    else:
        action = np.argmax(q_values)
    return action

3. BetaThompson sampling算法：

import numpy as np

class BetaThompsonSampling:
    def __init__(self, num_actions):
        """
        Beta Thompson sampling algorithm for multi-armed bandit problem.
        :param num_actions: number of actions (arms)
        """
        self.alpha = np.ones(num_actions)
        self.beta = np.ones(num_actions)

    def action_selection(self):
        """
        Select action according to the Beta distribution of each arm.
        :return: selected action
        """
        samples = np.random.beta(self.alpha, self.beta)
        action = np.argmax(samples)
        return action

    def update(self, action, reward):
        """
        Update the Beta distribution of the selected arm.
        :param action: selected action
        :param reward: observed reward
        """
        if reward == 1:
            self.alpha[action] += 1
        else:
            self.beta[action] += 1

4. UCB算法：

import numpy as np

class UCB:
    def __init__(self, num_actions, c=1.0):
        """
        Upper Confidence Bound (UCB) algorithm for multi-armed bandit problem.
        :param num_actions: number of actions (arms)
        :param c: exploration parameter
        """
        self.num_actions = num_actions
        self.c = c
        self.N = np.zeros(num_actions)
        self.Q = np.zeros(num_actions)

    def action_selection(self):
        """
        Select action according to the UCB upper confidence bound.
        :return: selected action
        """
        upper_bounds = self.Q + self.c * np.sqrt(np.log(np.sum(self.N)) / (self.N + 1e-8))
        action = np.argmax(upper_bounds)
        return action

    def update(self, action, reward):
        """
        Update the estimated action value of the selected arm.
        :param action: selected action
        :param reward: observed reward
        """
        self.N[action] += 1
        self.Q[action] += (reward - self.Q[action]) / self.N[action]

5. LinUCB算法：

import numpy as np

class LinUCB:
    def __init__(self, num_actions, num_features, alpha=0.1):
        """
        Linear Upper Confidence Bound (LinUCB) algorithm for multi-armed bandit problem.
        :param num_actions: number of actions (arms)
        :param num_features: number of features
        :param alpha: exploration parameter
        """
        self.num_actions = num_actions
        self.num_features = num_features
        self.alpha = alpha
        self.A = np.array([np.eye(num_features) for _ in range(num_actions)])
        self.b = np.zeros((num_actions, num_features))
        self.theta = np.zeros((num_actions, num_features))

    def action_selection(self, features):
        """
        Select action according to the LinUCB upper confidence bound.
        :param features: numpy array of shape (num_features,) representing the features of the context
        :return: selected action
        """
        upper_bounds = np.zeros(self.num_actions)
        for i in range(self.num_actions):
            A_inv = np.linalg.inv(self.A[i])
            self.theta[i] = np.dot(A_inv, self.b[i])
            upper_bounds[i] = np.dot(self.theta[i], features) + \
                              self.alpha * np.sqrt(np.dot(features.T, np.dot(A_inv, features)))
        action = np.argmax(upper_bounds)
        return action

    def update(self, action, features, reward):
        """
        Update the estimated parameters of the selected arm.
        :param action: selected action
        :param features: numpy array of shape (num_features,) representing the features of the context
        :param reward: observed reward
        """
        self.A[action] += np.outer(features, features)
        self.b[action] += reward * features