POPGym is designed to benchmark memory in deep reinforcement learning. It contains a set of environments and a collection of memory model baselines. The environments are all Partially Observable Markov Decision Process (POMDP) environments following the Openai Gym interface. Our environments follow a few basic tenets:
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DBE-KT22 contains student exercise answering activities collected through an online practicing platform for the database systems course taught at the Australian National University within the period 2018-2021. The dataset is useful for research targeting students' knowledge tracing given historical sequences of exercise answering.
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Panoramic Video Panoptic Segmentation Dataset is a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving. The dataset has labels for 28 semantic categories and 2,860 temporal sequences that were captured by five cameras mounted on autonomous vehicles driving in three different geographical locations, leading to a total of 100k labeled camera images.