I haven't played around with it much, but look at the layers of the network. First layer is the inputs which is pretty much one for each input square. If you

I haven't played around with it much, but look at the layers of the network. First layer is the inputs which is pretty much one for each input square. If you want to be fairly optimal figure you need to look at 5 lanes total and at least 10 squares ahead (and maybe a few behind if you need to get out of a jam) so that's 50 inputs per frame but it is taking inputs from 4 frames so that's 200+ inputs in the first layer (plus 15 for the actions for the last 3 frames). The output actions are 5 so that is the size of the output layer. You can change the number of nodes in the middle layer but you can also cut-and-past that code to add additional layers. I think you probably need at least two layers, the first to determine which lanes are clear and the second for figuring out how to maneuver into the desired lane. With such a large network, training will take longer so stick with the default iterations until you feel like you need more fine tuning.