Congrats on finishing the project! As you've already linked at the bottom of your post, it's possible that OpenAI could've solved most of your I/O issues.
One thing I'd suggest is exploring a reward function, instead of using only pre-recorded training data. That is, give the AI a goal to complete (in this case, finish the race) and let it learn by itself!
OpenAI's example universe agent. Remember that while their goal is an agent that works in any and all environments (read: games), you could certainly optimize yours just for MarioKart.
Comments
Congrats on finishing the project! As you've already linked at the bottom of your post, it's possible that OpenAI could've solved most of your I/O issues.
One thing I'd suggest is exploring a reward function, instead of using only pre-recorded training data. That is, give the AI a goal to complete (in this case, finish the race) and let it learn by itself!
I would love to learn how to do that - any suggestions?
EDIT: to clarify: what should I google for?
Here's what I could find in a couple minutes:
https://github.com/openai/universe-starter-agent
OpenAI's example universe agent. Remember that while their goal is an agent that works in any and all environments (read: games), you could certainly optimize yours just for MarioKart.
Thanks, looks promising! Can't wait to try it! :)
Reinforcement Learning. Here's a good intro: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
Perfect, thank you!!! You made my day. :-D
...later found this nice explanation of RL concepts if it helps someone: https://www.nervanasys.com/demystifying-deep-reinforcement-l...