{"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/curl-contrastive-unsupervised-representation","title":"CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning","arxiv_id":null,"date":"2020-01-01","proceeding":"ICML 2020 1","authors":["Michael Laskin","Pieter Abbeel","Aravind Srinivas"],"abstract":"Reinforcement Learning for control tasks where the agent learns from raw high dimensional pixels has proven to be difficult and sample-inefficient.  Operating on high-dimensional observational input poses a challenging credit assignment problem, which hinders the agent’s ability to learn optimal policies quickly. One promising approach to improve the sample efficiency of image-based RL algorithms is to learn low-dimensional representations from the raw input using unsupervised learning. To that end, we propose a new model: Contrastive Unsupervised Representation Learning for Reinforcement Learning (CURL). CURL extracts high level features from raw pixels using a contrastive learning objective and performs off-policy control on top of the extracted features. CURL achieves state-of-the-art performance and is the first image based algorithm across both model-free and model-based settings to nearly match the sample-efficiency and performance of state-based features on five out of the six DeepMind control benchmarks.","url_abs":"https://proceedings.icml.cc/static/paper_files/icml/2020/5951-Paper.pdf","url_pdf":"https://proceedings.icml.cc/static/paper_files/icml/2020/5951-Paper.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":"curl-contrastive-unsupervised-representation","repo_url":"https://github.com/MishaLaskin/curl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"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":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}