RLeXplore
RL toolkit
Provides a unified toolkit for constructing, computing, and optimizing intrinsic reward modules in reinforcement learning
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).
373 stars
4 watching
16 forks
Language: Jupyter Notebook
last commit: almost 2 years agoLinked from 1 awesome list
baselinesefficient-algorithmexploration-strategygymmachine-learningpybulletpytorchreinforcement-learningroboticstoolbox
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