reinforcement learning plot_rl(sag ar,steps_explore= ,steps_exploit= ,label="ar alpha="+str(sag ar alpha)sag ar q_ar_learner(senv, ,explore= ,fixed_alpha=falseplot_rl(sag ar,steps_explore= ,steps_exploit= ,label="ar alpha= / "sag ar q_ar_learner(senv, ,explore= ,fixed_alpha=false,alpha_fun=lambda : /( + )plot_rl(sag ar,steps_explore= ,steps_exploit= ,label="ar alpha= /( + )" model-based reinforcement learner to run the demoin folder "aipython"load "rlmodellearner py"and copy and paste the example queries at the bottom of that file this assumes python model-based reinforcement learner builds markov decision process model of the domainsimultaneously learns the model and plans with that model the model-based reinforcement learner used the following data structuresq[sais dictionary thatgiven (sapair returns the -valuethe estimate of the future (discountedvalue of being in state and doing action [sais dictionary thatgiven (sapair returns the average reward from doing in state [sasis dictionary thatgiven (sastuple returns the number of times was done in state swith the result being state svisits[sais dictionary thatgiven (sapair returns the number of times action was carried out in state res states[sais dictionary thatgiven (sapair returns the list of resulting states that have occurred when action was carried out in state this is used in the asynchronous value iteration to determine the sstates to sum over visits list is list of (sapair that have been carried out this is used to ensure there is no divide-by zero in the asynchronous value iteration note that this could be constructed from rvisits or res states by enumerating the keysbut needs to be list for random choiceand we don' want to keep recreating it version june