Reinforcement Learning, Artificial Intelligence, Machine Learning, Data Science, Statistics.
epsilon_greedy(state, time): epsilon = - 1/ (1 + np.exp((-time+7500000)/1700000)) + 1 z = np.random.random() if z > epsilon: action = max(Q_dict[state],key=Q_dict[state].get) #Exploitation: this gets the action corresponding to max q-value of current state else: action =