reinforcement learning agt q_learner(envt agt do( from rlsimpleenv import simple_game_env senv simple_game_env(sag q_learner(senv, ,explore= ,fixed_alpha=true,alpha= plot_rl(sag ,steps_explore= ,steps_exploit= ,label="alpha="+str(sag alpha)sag q_learner(senv, ,explore= ,fixed_alpha=falseplot_rl(sag ,steps_explore= ,steps_exploit= ,label="alpha= / "sag q_learner(senv, ,explore= ,fixed_alpha=false,alpha_fun=lambda : /( + )plot_rl(sag ,steps_explore= ,steps_exploit= ,label="alpha= /( + )" -leaning with experience replay warningnot properly dubugged rlqexperiencereplay py -linear reinforcement learner with experience replay from rlqlearner import q_learner from utilities import flip import random class boundedbuffer(object)def __init__(selfbuffer_size= )self buffer_size buffer_size self buffer [ ]*buffer_size self number_added def add(self,experience)if self number_added self buffer_sizeself buffer[self number_addedexperience elseif flip(self buffer_size/self number_added)position random randrange(self buffer_sizeself buffer[positionexperience self number_added + def get(self)return self buffer[random randrange(min(self number_addedself buffer_size)) class q_ar_learner(q_learner)def __init__(selfenvdiscountexplore= fixed_alpha=truealpha= alpha_fun=lambda : /kqinit= label="q_ar_learner"max_buffer_size= num_updates_per_action= burn_in= )version june