[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:def onpolicy_one_step(env, num_episodes, Q= defaultdict(lambda: np.zeros(env.action_space.n)), discount = 0.8):\nr = np.zeros(num_episodes)\nfor i_episode in range(1,num_episodes+1):\nepisode_reward = 0\nif (i_episode%1000)==0:\nprint("\\rEpisode {}/{}.".format(i_episode, num_episodes), end="")\nsys.stdout.flush()\nalpha = 0.3# 1-i_episode/num_episodes\nepsilon = 0.1# (1-i_episode/num_episodes) ** 2\nstate = env.reset()\npolicy = epsilon_greedy_policy(Q, state, env.action_space.n, epsilon)\naction = np.random.choice(np.array(len(policy)), p=policy)\nwhile True:\nnext_state, reward, done, _= env.step(action)\npolicy = epsilon_greedy_policy(Q, next_state, env.action_space.n, epsilon)\nnext_action = np.random.choice(np.array(len(policy)), p=policy)\nQ[state][action] = Q[state][action] + alpha * (reward + discount * Q[next_state][next_action] - Q[state][action])\nstate = next_state; action = next_action;\nepisode_reward += reward\nif done:\nbreak\nr[i_episode-1] = episode_reward + 1\nreturn Q, r'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]