{"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/exchangeable-models-in-meta-reinforcement","title":"Exchangeable Models in Meta Reinforcement Learning","arxiv_id":null,"date":"2020-06-12","proceeding":"ICML Workshop LifelongML 2020 7","authors":["Iryna Korshunova","Jonas Degrave","Joni Dambre","Arthur Gretton","Ferenc Huszar"],"abstract":"One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control. The former component is often based on recurrent neural networks, which do not directly exploit the exchangeable structure of the inputs. We propose to use a lightweight, yet an expressive architecture that accounts for exchangeability. Combined with an off-policy reinforcement learning algorithm, it results in a meta-RL method that is sample-efficient, fast to train and able to quickly adapt to new test tasks as demonstrated on a couple of widely used benchmarks. ","url_abs":"https://openreview.net/forum?id=TZFlzejFPT","url_pdf":"https://openreview.net/pdf?id=TZFlzejFPT","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":"exchangeable-models-in-meta-reinforcement","repo_url":"https://github.com/irakorshunova/bruno-sac","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"meta-reinforcement-learning","task_name":"Meta Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}