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RLeXplore

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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).

baselinesefficient-algorithmexploration-strategygymmachine-learningpybulletpytorchreinforcement-learningroboticstoolbox
Creat：2022-09-19T19:38:16
Update：2025-03-22T00:11:06
https://docs.rllte.dev/
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