Reinforcement Learning, Artificial Intelligence, Machine Learning, Data Science, Statistics.
self.gradient: exp_est = np.exp(self.q_estimation) self.action_prob = exp_est / np.sum(exp_est) return np.random.choice(self.indices, p=self.action_prob) q_best = np.max(self.q_estimation) return np.random.choice(np.where(self.q_estimation == q_best)[0]) step: Takes an action, generates a reward,