RLeXplore

Public

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).

baselines efficient-algorithm exploration-strategy gym machine-learning pybullet pytorch reinforcement-learning robotics toolbox

Creat：2022-09-19T19:38:16

Update：2025-03-22T00:11:06

https://docs.rllte.dev/

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