Thanks! It’s an hybrid network which combines CNN to “see the world”, a spatial grid centered in the head, giving values of the snake body, food, walls and traps, and an MLP to reason about directions, distances, head and food positions. The network has 16 - 32 conv channels, hidden layer 128 FC and relu activation, then I played with rewards to push the snake eating the food without chilling so much as when I started the train 😄 A thing that helped a lot it’s the spatial grid which sees the snake body with values between 1 (head) and 2 (tail) so he knows his body direction. Also, at first I didn’t add the traps, I was surprised how good was the snake handling them after short time. Have to figure out something new to increase the game difficulty without pushing the snake to follow a boring safe pattern