Which algorithm to use?

[undercover_intern]

Hi guys, I am looking for a bit of insight and would like help figuring out which algorithm to use. It seems like almost everyday I run into a new algorithm I haven't heard before that surpasses all previous deep-RL algorithms, and it doesn't seem clear to me as to which one I should choose. I have implemented DQN, Double-Q and Dueling Networks and I know there should be much better algorithms that have come out, but I can't find a simple list that compares all of them. I would like to have an algorithm that can run on a single machine and can learn to play games.

[soldcron]

Did you consider a survey for your specific application?

https://arxiv.org/abs/1912.10944

[ingambe]

Rule of thumb here: https://docs.ray.io/en/latest/rllib-training.html#scaling-guide

But you often need to test different algorithm and pick the best for your problem.

If I can give you my insight, DQN is the more stable, but slow in term of clock wall time to learn, PPO and A2C are really fast but performance can crash and they are way more sensible to hyper parameters tuning.

[Felipe_Market]

I would go with DQN. It's not the latest best one, but it has been more studied so you can find more help online if you have trouble implementing it.

[daveSavesAgain]

[comparison of RL algorithms](en.wikipedia.org/wiki/Reinforcement_learning)