{"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/automatic-goal-generation-for-reinforcement","title":"Automatic Goal Generation for Reinforcement Learning Agents","arxiv_id":"1705.06366","date":"2017-05-17","proceeding":"ICML 2018 7","authors":["Carlos Florensa","David Held","Xinyang Geng","Pieter Abbeel"],"abstract":"Reinforcement learning is a powerful technique to train an agent to perform a\ntask. However, an agent that is trained using reinforcement learning is only\ncapable of achieving the single task that is specified via its reward function.\nSuch an approach does not scale well to settings in which an agent needs to\nperform a diverse set of tasks, such as navigating to varying positions in a\nroom or moving objects to varying locations. Instead, we propose a method that\nallows an agent to automatically discover the range of tasks that it is capable\nof performing. We use a generator network to propose tasks for the agent to try\nto achieve, specified as goal states. The generator network is optimized using\nadversarial training to produce tasks that are always at the appropriate level\nof difficulty for the agent. Our method thus automatically produces a\ncurriculum of tasks for the agent to learn. We show that, by using this\nframework, an agent can efficiently and automatically learn to perform a wide\nset of tasks without requiring any prior knowledge of its environment. Our\nmethod can also learn to achieve tasks with sparse rewards, which traditionally\npose significant challenges.","url_abs":"http://arxiv.org/abs/1705.06366v5","url_pdf":"http://arxiv.org/pdf/1705.06366v5.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":"automatic-goal-generation-for-reinforcement","repo_url":"https://github.com/jeffchy/Artificial-Idiot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"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":"https://app.syntology.ai/?focus=1705.06366","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}