{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/s-rl-toolbox-environments-datasets-and","title":"S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning","arxiv_id":"1809.09369","date":"2018-09-25","proceeding":null,"authors":["Antonin Raffin","Ashley Hill","René Traoré","Timothée Lesort","Natalia Díaz-Rodríguez","David Filliat"],"abstract":"State representation learning aims at learning compact representations from\nraw observations in robotics and control applications. Approaches used for this\nobjective are auto-encoders, learning forward models, inverse dynamics or\nlearning using generic priors on the state characteristics. However, the\ndiversity in applications and methods makes the field lack standard evaluation\ndatasets, metrics and tasks. This paper provides a set of environments, data\ngenerators, robotic control tasks, metrics and tools to facilitate iterative\nstate representation learning and evaluation in reinforcement learning\nsettings.","url_abs":"http://arxiv.org/abs/1809.09369v2","url_pdf":"http://arxiv.org/pdf/1809.09369v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"s-rl-toolbox-environments-datasets-and","repo_url":"https://github.com/araffin/robotics-rl-srl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"s-rl-toolbox-environments-datasets-and","repo_url":"https://github.com/araffin/srl-zoo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"s-rl-toolbox-environments-datasets-and","repo_url":"https://github.com/billchan226/poar-srl-4-robot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"s-rl-toolbox-environments-datasets-and","repo_url":"https://github.com/eric-erki/robotics-rl-srl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"s-rl-toolbox-environments-datasets-and","repo_url":"https://github.com/kalifou/robotics-rl-srl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.09369","atlas_url":"https://app.syntology.ai/?focus=1809.09369","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}