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).
Explore and compare different methods for teaching AI to learn efficiently.
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Free · no card · unsubscribe anytimeRLeXplore 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).
RLeXplore has 465 stars on GitHub. It has been forked 23 times. RLeXplore is written mainly in Jupyter Notebook. It has been in active development since 2022. RLeXplore is available under the MIT license. Its main topics are baselines, efficient-algorithm, exploration-strategy, gym.
What is 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).
Is RLeXplore open source?
RLeXplore is an open-source project. It is released under the MIT license.
Is RLeXplore free?
Yes. RLeXplore is free and open source — you can use, modify and self-host it.
What license does RLeXplore use?
RLeXplore is available under the MIT license.
What language is RLeXplore written in?
RLeXplore is written mainly in Jupyter Notebook.
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