Question Creating an FSM agent in the dodgeball environment

Hello,

I would like to create a simpler agent for benchmark purposes. I am using NEAT to create new agents for my masters thesis, but i need an agent that is similar to a standard game NPC to measure the results from the NEAT agent against.

The environment i want to make the agent for is the ml-agents dodgeball environment. I want to note that I have manipulated the environment to be a 1v1 environment instead of the 4v4 elimination mode that is included in the dodgeball environment. Ideally I just want an agent that is able to play the game against a human opponent.

I am not well versed in unity, so i was wondering if there was a simple and time efficient way to do this. If anyone knows of any frameworks/previously created FSM or rule based agents/general conventional approach to this problem, it would help tons!

Here is the original Dodgeball environment:
https://github.com/Unity-Technologies/ml-agents-dodgeball-env
Here is my repo:
https://github.com/Hallahallan/Dodgeball-Bio-fMRI

Answers or discussions around the topic are appreciated.

 

Hi there,

It's great to hear about your master's thesis project involving NEAT agents and the ml-agents dodgeball environment.
Creating a simplified agent for benchmarking purposes in your NEAT-based master's thesis project is a wise approach. To achieve this, considering your specific requirements for the ml-agents dodgeball environment, you might opt for a rule-based agent. You can start by defining basic decision rules and strategies using Unity's scripting capabilities. Additionally, explore Unity assets and community-created frameworks like "Hutong Games' Playmaker" or "NodeCanvas" to streamline the implementation process.

As you delve into this exciting project, don't forget to consult the ml-agents dodgeball environment documentation for any integration specifics. Best of luck with your thesis work; I hope this guidance proves valuable in your research! ️