<ul><li>Offline reinforcement learning has become popular for learning control policies from pre-collected data.</li><li>PyTupli, a Python-based tool, has been introduced to streamline the creation, storage, and sharing of benchmark environments and datasets for offline RL projects.</li><li>PyTupli includes a client library for uploading and retrieving benchmarks and data, along with support for fine-grained filtering at the episode and tuple level.</li><li>The tool aims to enhance collaborative, reproducible, and scalable offline RL research by addressing key barriers in dataset infrastructure.</li></ul>

PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects

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