Reinforcement Learning Evolution Foundation
A foundation for the evolution of advanced reinforcement learning.
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- Hong Kong
- mingqi.yuan@connect.polyu.hk
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- 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).
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