If you would like to incorporate strategy and planning into the AI, there are many methods. All of them get more difficult if the AI have to work together.
I just finished CS-6600 (Intelligent systems) in which I made a sword fighting AI here on Roblox. The method, Q-Learning, is applicable for your AIs as well. If you have maps that make some positions more favorable then you may want to augment the Q-Learner with a neural network to help predict state values. The reasons why are explained in a report I put together. Here is a video of the AI training:
Here is the repo with all the source code:
Here is the class report (put together in 30 minutes, don’t tell my professor!) about what issues I ran into and what I learned:
https://drive.google.com/file/d/1MbCnC22bmX5uQZtBNHx1BKECHM_Sygu0/view?usp=sharing
And here is an article I found useful to learn about Q-learning:
Lastly, here is the place file:
AI.rbxl (80.5 KB)
To summarize, Q-Learning learns the best “policy”, for a given state (at a specific location on a map, enemy’s position, reload times, fuel, health, ext) what is the best action to take? It learns the best actions by trial and error over time. This AI was trained with a fairly small number of states so learns really fast. Other AIs may require more training or even a neural network to estimate state values. The long-term planning comes in via the propagation of rewards back along the states preceding it.