[D] New to machine learning: I have a few questions
I’m a game developer, and with my team at the studio we are doing a bit of r&d to find the fittest (see what I did there) solution to have an AI system for our enemies in in a top down 2D game that would feel organic and unpredictable. We are trying to see if ml agents in Unity could be better suited than the heuristic approach. My main question is:
We are trying to setup the input layer, and we hesitate on how much data we can feed to the ml agents of unity, here is our data:
- 10 raycasts shot radially in front of the player, giving distance to the walls (only) it touches
- Bullet angle of the closest bullets (*4)
- Bullet distance of the closest bullets (*4)
- Bullet orientation (0 is 90° up from enemy – 0.5 is dead on, 1 is 90° down) (*4)
- Player angle (*6)
- Player distance (*6)
- Player shield (*6)
- Player life (*6)
- Player combat mode (4 different modes, sword/shield – gun/shield (etc), 4 modes, input values increasing by 0.25 (*6)
We are trying to figure out what’s more important, and what to add
By ‘angle’, I’m implying the angle between the nose of the enemy and the object (player/bullet)
that means 46 potential inputs for our NN.
the player(s) will fight in a coop games, with up to 6 / 8 enemies on screen using non heuristic brains, and other classical AI.
that must be able to run real time with average graphic cards. The game is 2D and doesn’t require too much power from cpu/gpu.
- 30~40 inputs * 8 brains in real time on average graphic cards: too much?
- How can we hope to go?
- Any tips on how to think about sizing our NNs ?
- Are you having a good day?
Thank you for the read =)