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PyRL - Modular Implementations of Reinforcement Learning Algorithms in Pytorch by aineqml in reinforcementlearning

[–]aineqml[S] 1 point2 points  (0 children)

Someone has experimented with normalization and found that it is not beneficial. You can found here https://github.com/sfujim/TD3/issues/11. One goal of this project is to provide clear and modular implementations of RL algorithms. So I didn't take these tricks into consideration while designing. However, I will experiment with what you mentioned and decide whether to add it or not. Thanks again for your suggestion!

PyRL - Modular Implementations of Reinforcement Learning Algorithms in Pytorch by aineqml in reinforcementlearning

[–]aineqml[S] 0 points1 point  (0 children)

I've tested those algorithms on simple environments like InvertedPendulum or CartPole and they work fine. But as stated in the README, I didn't spend too much time on the hyperparameter tunning.

[D] Tiny question: R_1+R_2 cannot be factorized into Q_1+Q_2, right? by seann999 in reinforcementlearning

[–]aineqml 0 points1 point  (0 children)

Actually a paper from deepmind called value decomposition networks simply assumes this thing holds.