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This project is an AI research implementation library and machine learning research repository. It provides a collection of reference code, illustrative implementations, and open-source research datasets used to verify hypotheses and build upon existing models in artificial intelligence. The repository focuses on scientific research reproduction by translating theoretical findings from published papers into executable code. It includes specialized scientific simulation environments designed to test the behavior of autonomous agents and models within controlled settings. The project covers AI
This is an original PyTorch implementation of the ExORL framework from
RL4RS is a real-world deep reinforcement learning recommender system benchmark for practitioners and researchers.
Kajetan Schweighofer 1 , Markus Hofmarcher 1 , Marius-Constantin Dinu 1,3 , Philipp Renz 1 , Angela Bitto-Nemling 1 , Vihang Patil 1 , Sepp Hochreiter 1, 2
RLDS stands for Reinforcement Learning Datasets and it is an ecosystem of tools to store, retrieve and manipulate episodic data in the context of Sequential Decision Making including Reinforcement Learning (RL), Learning for Demonstrations, Offline RL or Imitation Learning.
The main features of google-research/rlds are: Datasets and Benchmarks.
Open-source alternatives to google-research/rlds include: deepmind/deepmind-research — This project is an AI research implementation library and machine learning research repository. It provides a… denisyarats/exorl — This is an original PyTorch implementation of the ExORL framework from. fuxiailab/rl4rs — RL4RS is a real-world deep reinforcement learning recommender system benchmark for practitioners and researchers. ml-jku/offlinerl — Kajetan Schweighofer 1 , Markus Hofmarcher 1 , Marius-Constantin Dinu 1,3 , Philipp Renz 1 , Angela Bitto-Nemling 1 ,… rail-berkeley/d4rl — D4RL is an open-source benchmark for offline reinforcement learning. It provides standardized environments and… securitygames/oef — Repository for the submission of NeurIPS Datasets and Benchmarks Track 2022.