exercise it is possible to implement the model-based reinforcement learner by replacing qrvisitsres states with single dictionary that returns tuple (qrvtmwhere qr and are numbersand tm is map from resulting states into counts does this make the algorithm easier to understanddoes this make the algorithm more efficientexercise if the states and the actions were mapped into integersthe dictionaries could be implemented more efficiently as arrays this entails an extra step in specifying problems implement this for the simple game is it more efficient reinforcement learning with features to run the demoin folder "aipython"load "rlfeatures py"and copy and paste the example queries at the bottom of that file this assumes python representing features feature is function from state and action to construct the features for domainwe construct function that takes state and an action and returns the list of all feature values for that state and action this feature set is redesigned for each problem get features(stateactionreturns the feature values appropriate for the simple game rlsimplegamefeatures py -feature-based reinforcement learner from rlsimpleenv import simple_game_env from rlproblem import rl_env def get_features(state,action)"""returns the list of feature values for the state-action pair ""assert action in simple_game_env actions ( , , ,pstate would go to monster monster_ahead( , ,actionf would crash into wall wall_ahead( , ,actionf action is towards prize towards_prize( , ,action,pf damaged and action is toward repair station towards_repair( , ,actionif else damaged and towards monster if and else damaged if else not damaged version june