resizing itchecking "show -valuesand "show policy"and clicking "stepa few times exercise computing before may seem like waste of space because we don' need to store in order to compute value function or the policy change the algorithm so that it loops through the states and actions once per iterationand only stores the value function and the policy note that to get the same results as beforeyou would need to make sure that you use the previous value of in the computation not the current value of does using the current value of hurt the algorithm or make it better (in approaching the actual value function)asynchronous value iteration this implements asynchronous value iterationstoring function is represented so [ ][ais the value for doing action with index state with index mdpproblem py -(continued def avi(self, )states list(self statesactions list(self actionsfor in range( ) random choice(statesa random choice(actionsself [ ][ (self ( ,aself discount sum( max(self [ ][ for in self actionsfor ( , in self ( ,aitems())return the following shows how avi can be used mdpexamples py -(continued #testing asynchronous value iteration try the followingpt party(discount= pt avi( pt vi( gr grid( gr avi( [( , )exercise implement value iteration that stores the -values rather than the -values does it work better than storing (what might better mean?exercise in asynchronous value iterationtry number of different ways to choose the states and actions to update ( sweeping through the state-action pairschoosing them at randomnote that the best way may be to determine version june