Reinforcement Learning, Artificial Intelligence, Machine Learning, Data Science, Statistics.
np.zeros(self.k) + self.initial # # of chosen times for each action self.action_count = np.zeros(self.k) self.best_action = np.argmax(self.q_true) self.time = 0 act: Selects an action based on the specified algorithm (epsilon-greedy, UCB, or gradient-based). def act(self): if np.random.rand() <