<prompt> Bakeng sa dataset e fanoeng, hlahisa Reinforcement Learning Algorithm ho bolela esale pele boleng ba kamoso ba setoko.Dataset: Litheko tsa ho koala tsa letsatsi le letsatsi tsa setoko sa Apple Inc. nakong ea likhoeli tse 12.  <response>: Instruction in English: For the given dataset, develop a Reinforcement Learning Algorithm to predict the future value of the stock. . Response in English: To predict future stock prices, we can use a type of reinforcement learning RL called Q-Learning. It is an algorithm for learning a value function, which tells an agent which action to take under what circumstances, by examining the possible future rewards of its actions.The Q-learning algorithm involves learning a function that takes a state-action pair and returns the expected future reward of that action. Here, a state could be a combination of the past returns of the stock, while action can be to buy or sell the stock. The algorithm works as follows:1. Initialize the Q-table - this is a table that holds the Q values for each state-action pair.2. For each episode i.e., trading session: a. Set the initial state. b. Choose an action based on the current state using an explorationexploitation strategy e.g., epsilon-greedy. c. Take the action and observe the new state and the reward. d. Update the Q-table using the Bellman equation: Qs,a Qs,a alphareward gammamaxQs, a - Qs,a, where alpha is the learning rate, gamma is the discount factor and s is the new state. e. Set the state to the new state and repeat until the episode ends.3. Use the Q-table to determine the action to take for a given state.The above approach is model-free, which means that it does not require any knowledge of the underlying distribution of stock prices. Instead, it learns by interacting with the environment and updating its Q-values based on the rewards it receives.You should note that developing a model to predict stock prices is a complex task and depends on many variables. The above Q-Learning model is just one approach, and its performance will depend on several factors such as the explorationexploitation strategy used, choice of the state representation, learning rate, discount factor, and the market conditions. Also, RL models ideally require large datasets to train on, so the model performance might not be optimal if the dataset is only for 12 months. . Response in Sesotho: Ho bolela esale pele litheko tsa nakong e tlang, re ka sebelisa mofuta oa thuto ea matlafatso RL e bitsoang Q-Learning. Ke algorithm bakeng sa ho ithuta tsebetso ea boleng, e bolellang moemeli hore na a nke khato efe tlasa maemo afe, ka ho hlahloba meputso e ka bang teng nakong e tlang ea liketso tsa eona. Q-learning algorithm e kenyelletsa ho ithuta tšebetso e nkang boemo ba boemo le ho khutlisa moputso o lebelletsoeng oa ketso eo. Mona, mmuso e ka ba motsoako oa li-stock tsa nakong e fetileng, athe ketso e ka ba ho reka kapa ho rekisa thepa. Algorithm e sebetsa ka tsela e latelang: 1. Qala tafole ea Q - ena ke tafole e nang le litekanyetso tsa Q bakeng sa para e ngoe le e ngoe ea boemo. 2. Bakeng sa karolo ka ngoe ke hore, nako ea khoebo: a. Beha boemo ba pele. b. Khetha ketso e ipapisitseng le boemo ba hajoale u sebelisa leano la ho phenya-phenyatlatlapa mohlala, epsilon-greedy. c. Nka khato me u shebe boemo bo bocha le moputso. d. Kaonefatsa tafole ea Q u sebelisa equation ea Bellman: Qs,a Qs,a alphamoputso gammamaxQs, a - Qs,a , moo alpha e leng sekhahla sa ho ithuta, gamma ke ntlha ea theolelo me s ke naha e ncha. e. Beha boemo boemong bo bocha me u phete ho fihlela ketsahalo e fela. 3. Sebelisa tafole ea Q ho fumana hore na ketso efe e lokelang ho etsoa bakeng sa naha e fanoeng. Mokhoa o ka holimo ha o na mohlala, o bolelang hore ha o hloke tsebo leha e le efe ea kabo ea motheo ea theko ea theko. Sebakeng seo, e ithuta ka ho sebelisana le tikoloho le ho nchafatsa boleng ba eona ba Q ho latela meputso eo e e fumanang. U lokela ho hlokomela hore ho hlahisa mohlala ho bolela esale pele litheko tsa theko ke mosebetsi o rarahaneng me o itšetlehile ka mefuta e mengata. Mohlala o ka holimo oa Q-Learning ke mokhoa o le mong feela, me tsebetso ea oona e tla itšetleha ka lintlha tse maloa tse kang leano la ho hlahloba ho tlatlapa le sebelisitsoeng, khetho ea boemeli ba mmuso, tekanyo ea ho ithuta, ntlha ea theolelo, le maemo a maraka. Hape, mefuta ea RL e hloka li-datasets tse kholo ho ikwetlisa ka tsona, kahoo tsebetso ea mohlala e kanna ea se sebetse hantle haeba datha e le ea likhoeli tse 12 feela. 