RLE-Foundation / RLeXplore

RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
https://docs.rllte.dev/
MIT License
367 stars 15 forks source link

code for paper #19

Closed hlsafin closed 3 months ago

hlsafin commented 3 months ago

Can you provide some of the paper's code so it can be reproduced? I am having difficulties reproducing them.

yuanmingqi commented 3 months ago

pls refer to this branch: https://github.com/RLE-Foundation/rllte/tree/reward