the better way soa better way is to introduce little bit of random variation into my actions as ' exploring sowe call that an epsilon term sosuppose we have some valuethat roll the dicei have random number if it ends up being less than this epsilon valuei don' actually follow the highest valuei don' do the thing that makes sensei just take path at random to try it outand see what happens that actually lets me explore much wider range of possibilitiesa much wider range of actionsfor wider range of states more efficiently during that exploration stage sowhat we just did can be described in very fancy mathematical termsbut you know conceptually it' pretty simple fancy words explore some set of actions that can take for given set of statesi use that to inform the rewards associated with given action for given set of statesand after that exploration is done can use that informationthose valuesto intelligently navigate through an entirely new maze for example this can also be called markov decision process so againa lot of data science is just assigning fancyintimidating names to simple conceptsand there' ton of that in reinforcement learning markov decision process soif you look up the definition of markov decision processesit is " mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of decision makerdecision makingwhat action do we take given set of possibilities for given statein situations where outcomes are partly randomhmmkind of like our random exploration there partly under the control of decision makerthe decision maker is our values that we computed