Neural networks in general are basically gigantic, static formulas: get the input numbers, multiply & add a billion times, report the output numbers. What you're imagining is more like reinforcement learning, which is the kind of learning employed by intelligent agents. An intelligent agent acts within an environment, and actions that lead to good outcomes are reinforced. An agent balances exploration with exploitation; exploration means trying out new actions to see how well they work, while exploitation means performing actions that are known to work well.