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CORL is an open-source library that provides single-file implementations of Deep Offline Reinforcement Learning algorithms. It emphasizes a simple developing experience with a straightforward codebase and a modern analysis tracking tool. In CORL, we …

High-quality single-file implementations of SOTA Offline RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC

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In this work, we argue for the importance of an online evaluation budget for a reliable comparison of deep offline RL algorithms. First, we delineate that the online evaluation budget is problem-dependent, where some problems allow for less but …

Minimalistic framework to run machine learning experiments.

Time Management practices.

Minimalistic framework to run machine learning experiments

Month-long digital nomad trip to SA.

Over the past decade, tremendous progress has been made in inventing new RecSys methods. However, one of the fundamental problems of the RecSys research community remains the lack of applied datasets and benchmarks with well-defined evaluation rules …