AI-Toolbox VS pleuro

Makes sense. I was just wondering if someone had any comparisons to share. I will create a toy environment in Unreal and compare integrating RL C++ libraries (looking at AI-Toolbox and mlpack) vs using Python with socket communication.

I maintain a repository of many implementations of classical (tabular) RL algorithms [1] which you might enjoy playing with when starting out. I use it for both research and for student projects. The advantage of avoiding NNs when starting out is that it is much simpler to inspect the inner workings of an algorithm to see whether it's working or not.

I'm always happy to help if something is unclear or difficult so feel free to open issues there :)

[1]: https://github.com/Svalorzen/AI-Toolbox

> even markov chains can lead to behavior that looks this way.

What a fun project, thank you for sharing the link.

re:

> Once we created the creatures, we set up their odors, which enable our creatures to smell them. When we have odors and creatures all we then have to do is build a the control components

This sounds similar to the "collaborative diffusion" approach to pathfinding [1].

> However, it is possible to view the code on the github page.

The link to https://github.com/lettergram/pleuro does not work. Do you still have a copy of the code somewhere? I'm curious to learn more about how you modelled the control loops with markov chains.

Was the rough idea that there are states "forage", "eat", "protect", and then probability of transitions between states depends upon the simulated creature's current state & sensor information about the environment?

[1] Repenning 2006 "Collaborative diffusion: programming antiobjects" https://home.cs.colorado.edu/~ralex/papers/PDF/OOPSLA06antio...