{"url":"/dataset/carl-1","name":"CARL","full_name":"Context Adaptive RL","description_markdown":"CARL (context adaptive RL) provides highly configurable contextual extensions to several well-known RL environments. It's designed to test your agent's generalization capabilities in all scenarios where intra-task generalization is important.\r\n\r\nBenchmarks include:\r\n\r\n- [OpenAI gym classic control suite](/dataset/openai-gym) extended with several physics context features like gravity or friction\r\n\r\n- [OpenAI gym Box2D](/dataset/openai-gym) BipedalWalker, LunarLander and CarRacing, each with their own modification possibilities like new vehicles to race\r\n\r\n- All [Brax locomotion environments](/dataset/brax) with exposed internal features like joint strength or torso mass\r\n\r\n- [Super Mario (TOAD-GAN)](/dataset/toad-gan), a procedurally generated jump'n'run game with control over level similarity\r\n\r\n- [RNADesign](/dataset/rnadesign), an environment for RNA design given structure constraints with structures from different datasets to choose from\r\n\r\nDescription from: [CARL](https://github.com/automl/CARL)\r\n\r\nImage source: [https://github.com/automl/CARL](https://github.com/automl/CARL)","description_withheld":null,"homepage":"https://github.com/automl/CARL","introduced_date":"2021-10-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/carl-a-benchmark-for-contextual-and-adaptive","title":"CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning","first_author":"Carolin Benjamins","url":null},"license":{"name":"Apache License 2.0","url":"https://github.com/automl/CARL/blob/main/LICENSE"},"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[],"languages":[],"variants":["CARL"],"data_loaders":[{"repo":"https://github.com/automl/CARL","url":"https://github.com/automl/CARL","frameworks":[]}],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/face-recognition-on-carl","task":"Face Recognition","dataset_variant":"Carl","rows":2,"metrics":["Rank-1"],"first_row_in_archive_order":{"model":"Model with Up Convolution + DoG Filter (Aligned)","paper":"/paper/thermal-to-visible-face-recognition-using","metrics":{"Rank-1":"85"},"code_links":[{"title":"Faceplugin-ltd/FaceRecognition-Android","url":"https://github.com/Faceplugin-ltd/FaceRecognition-Android"},{"title":"Alpkant/Thermal-to-Visible-Face-Recognition-Using-Deep-Autoencoders","url":"https://github.com/Alpkant/Thermal-to-Visible-Face-Recognition-Using-Deep-Autoencoders"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/thermal-to-visible-face-recognition-using","title":"Thermal to Visible Face Recognition Using Deep Autoencoders","date":"2020-02-10","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-perceptual-mapping-for-cross-modal-face","title":"Deep Perceptual Mapping for Cross-Modal Face Recognition","date":"2016-01-20","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}