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Friedrich Yuanqgallouedec
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Add RLeXplore to the project page (#1246)
* Update project page Adding the repo "rl-exploration-baselines" to the project page. * Update changelog.rst * Update projects.rst * Update changelog.rst * Update docs/misc/projects.rst * Update changelog.rst Co-authored-by: Quentin Gallouédec <[email protected]>
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docs/misc/changelog.rst

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- Updated custom policy documentation (@athatheo)
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- Improved tensorboard callback doc
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- Clarify doc when using image-like input
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- Added RLeXplore to the project page (@yuanmingqi)
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Release 1.6.2 (2022-10-10)
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@eleurent @ac-93 @cove9988 @theDebugger811 @hsuehch @Demetrio92 @thomasgubler @IperGiove @ScheiklP
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@simoninithomas @armandpl @manuel-delverme @Gautam-J @gianlucadecola @buoyancy99 @caburu @xy9485
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@Gregwar @ycheng517 @quantitative-technologies @bcollazo @git-thor @TibiGG @cool-RR @MWeltevrede
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@Melanol @qgallouedec @francescoluciano @jlp-ue @burakdmb @timothe-chaumont @honglu2875
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@Melanol @qgallouedec @francescoluciano @jlp-ue @burakdmb @timothe-chaumont @honglu2875 @yuanmingqi
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@anand-bala @hughperkins @sidney-tio @AlexPasqua @dominicgkerr @Akhilez @Rocamonde @tobirohrer @ZikangXiong

docs/misc/projects.rst

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| GitHub: https://github.com/ac-93/tactile_gym
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| Paper: https://arxiv.org/abs/2106.08796
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| Website: `tactile-gym website <https://sites.google.com/my.bristol.ac.uk/tactile-gym-sim2real/home>`_
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RLeXplore
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---------
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RLeXplore is a set of implementations of intrinsic reward driven-exploration approaches in reinforcement learning using PyTorch, which can be deployed in arbitrary algorithms in a plug-and-play manner. In particular, RLeXplore is designed to be well compatible with Stable-Baselines3, providing more stable exploration benchmarks.
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- Support arbitrary RL algorithms;
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- Highly modular and high expansibility;
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- Keep up with the latest research progress.
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| Author: Mingqi Yuan
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| GitHub: https://github.com/yuanmingqi/rl-exploration-baselines

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