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).
AI summary
RL toolkit
Provides a unified toolkit for constructing, computing, and optimizing intrinsic reward modules in reinforcement learning
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