I am working on a DIY project in which I want to be able to train a neural network to play Snake.

  • Is a genetic algorithm an efficient way of training a network for this application?

  • For a GA, what should the inputs of the network be? (distance to walls and fruit or the squares in the proximity of the snake head as a vector)

  • What would the difference in efficiency be depending on the algorithm and what limitation does each one have? Are there any other alternatives I should consider?


Q-Learning and Genetic algorithm are both good algorithms to create an IA that plays Snake.

The one you use depends mostly on how you understand and model your IA environment.

  • Q-Learning algorithm is an algorithm that needs a State (give by the environment), Actions it can take, and Rewards to give him according to how it performs.
  • Genetic algorithm needs to have intrinsic parameters (could be caracteristics of the network taking the decisions, or more simple things like leg size / muscle strenght if you want to make an IA that runs), how to merge 2 parents to create children, and a Metric to evaluate how the network performed.

I assume your snake is make of a grid (like old snake where you only have 4 directions, you can't go in diagonal directions).

In snake example, here is how I would define it in both case (careful, this is how I would model the problem, there might be more efficient models) :


  • State : what is in each block of the grid (snake tail, snake head, food)
  • Actions : Up, Down, Right, Left
  • Rewards : Not dying : +0.01; dying : -10; eating piece of food: +1.

Genetic Algorithm

  • Intrinsic Parameters : Parameters of the network that takes decisions (inputs of the network could be whole state of the environment as described in Q-Table).
  • How to merge : ?
  • Metric : Lenght of the snake or time lived.

I can't find exactly how to make a genetic algorithm, but it should be possible.

Hope it helps. Q-Learning is usually easier so this is what I would use, but feel free to use whatever you want.


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