{"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/temporally-extended-e-greedy-exploration","title":"Temporally-Extended ε-Greedy Exploration","arxiv_id":"2006.01782","date":"2020-06-02","proceeding":"ICLR 2021 1","authors":["Will Dabney","Georg Ostrovski","André Barreto"],"abstract":"Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly complex solutions to the problem. This increase in complexity often comes at the expense of generality. Recent empirical studies suggest that, when applied to a broader set of domains, some sophisticated exploration methods are outperformed by simpler counterparts, such as {\\epsilon}-greedy. In this paper we propose an exploration algorithm that retains the simplicity of {\\epsilon}-greedy while reducing dithering. We build on a simple hypothesis: the main limitation of {\\epsilon}-greedy exploration is its lack of temporal persistence, which limits its ability to escape local optima. We propose a temporally extended form of {\\epsilon}-greedy that simply repeats the sampled action for a random duration. It turns out that, for many duration distributions, this suffices to improve exploration on a large set of domains. Interestingly, a class of distributions inspired by ecological models of animal foraging behaviour yields particularly strong performance.","url_abs":"https://arxiv.org/abs/2006.01782v1","url_pdf":"https://arxiv.org/pdf/2006.01782v1.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":"temporally-extended-e-greedy-exploration","repo_url":"https://github.com/oh-lab/UTE-Uncertainty-aware-Temporal-Extension-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.01782","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}