I'd like to understand on how to train a neural net with agents and evolution
I’d like to understand on how to train a neural net with agents and evolution.It might be easier to think of a game world though i don’t create games.The training will be inside jupyter data.Say I got 10 inputs, the the agent has some value (reward store).The 10 values are unknown and to be interpreted by a NNIt needs to improve its reward though its output is just 3 options, like left/forward/rightNot every move results in a reward so training likely takes time period.Depending on their reactions agents might be in a different scenario, though at some time one selects the best (n) agents. (high reward)And then trains again until a supergood agent is able to interpren the input valuesHow does one create train such a network?,Normally in neural networks one trains a network toward a certain goal, using back prop, ea several inputs to resolve something alike DNN or a LSTM. But the rules here are so different.Anyone knows of some jupyter sample for a DNN training alike that ?.