Bet yall haven't seen a neural net that looks like this before.
It doesn't do anything impressive but it uses the cellular automata structure I've talked about before. Every node is connected to the 8 or less nodes around it and updated simultaneously, not in series. The green nodes are controlled by sensors and the blue nodes are sampled as output.
The structure allows the AI to have a memory and iterate as part of problem solving. On multiple occasions when I had the reward system wrong, it had the ability to come to a complete stop in the road and then resume "driving." It is not told velocity info. The 5 inputs are 5 ray distances extending forward.
Of course the big negative of this structure is that information has to physically travel and overlap other information. The two green bars on the left have to send information across to the right blue bar which controls steering. I purposely designed the shape of this network to show this isn't a deal breaker, but the green cells can be placed on the bottom and the blue cells can be placed on the top to improve it. A regular neural network is much nicer because all inputs have equal potential within the system. Also the massive massive limitation of this setup is that traditional backpropagation is impossible.
It doesn't do anything impressive but it uses the cellular automata structure I've talked about before. Every node is connected to the 8 or less nodes around it and updated simultaneously, not in series. The green nodes are controlled by sensors and the blue nodes are sampled as output.
The structure allows the AI to have a memory and iterate as part of problem solving. On multiple occasions when I had the reward system wrong, it had the ability to come to a complete stop in the road and then resume "driving." It is not told velocity info. The 5 inputs are 5 ray distances extending forward.
Of course the big negative of this structure is that information has to physically travel and overlap other information. The two green bars on the left have to send information across to the right blue bar which controls steering. I purposely designed the shape of this network to show this isn't a deal breaker, but the green cells can be placed on the bottom and the blue cells can be placed on the top to improve it. A regular neural network is much nicer because all inputs have equal potential within the system. Also the massive massive limitation of this setup is that traditional backpropagation is impossible.
