reinforcement learning next_state,reward self env do(self actionself add_to_buffer((self state,self action,reward,next_state)#remember experience self acc_rewards +reward next_action self select_action(next_statefeature_values self get_features(self state,self actionoldq dot_product(self weightsfeature_valuesnextq dot_product(self weightsself get_features(next_state,next_action)delta reward self discount nextq oldq for in range(len(self weights))self weights[ +self step_size delta feature_values[iself display( ,self stateself actionrewardnext_statedot_product(self weightsfeature_values)deltasep='\ 'self state next_state self action next_action if self number_added self burn_infor in range(self num_updates_per_action)( , , ,nsself action_buffer[random randrange(min(self number_addedself max_buffer_size))na self select_action(nsfeature_values self get_features( ,aoldq dot_product(self weightsfeature_valuesnextq dot_product(self weightsself get_features(ns,na)delta reward self discount nextq oldq for in range(len(self weights))self weights[ +self step_size delta feature_values[ test coderllinexperiencereplay py -(continued from rlqtest import senv simple game environment from rlsimplegamefeatures import get_featuressimp_features from rlplot import plot_rl fa sarsa_lfa_ar_learner(senvget_features step_size= #fa max_display_level #fa do( #plot_rl(fa ,steps_explore= ,steps_exploit= ,label="sarsa_lfa_ar( )"fas sarsa_lfa_ar_learner(senvsimp_features step_size= #plot_rl(fas ,steps_explore= ,steps_exploit= ,label="sarsa_lfa_ar(simp)"version june