{"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/deep-reinforcement-learning-for-event","title":"Deep Reinforcement Learning for Event-Triggered Control","arxiv_id":"1809.05152","date":"2018-09-13","proceeding":null,"authors":["Dominik Baumann","Jia-Jie Zhu","Georg Martius","Sebastian Trimpe"],"abstract":"Event-triggered control (ETC) methods can achieve high-performance control\nwith a significantly lower number of samples compared to usual, time-triggered\nmethods. These frameworks are often based on a mathematical model of the system\nand specific designs of controller and event trigger. In this paper, we show\nhow deep reinforcement learning (DRL) algorithms can be leveraged to\nsimultaneously learn control and communication behavior from scratch, and\npresent a DRL approach that is particularly suitable for ETC. To our knowledge,\nthis is the first work to apply DRL to ETC. We validate the approach on\nmultiple control tasks and compare it to model-based event-triggering\nframeworks. In particular, we demonstrate that it can, other than many\nmodel-based ETC designs, be straightforwardly applied to nonlinear systems.","url_abs":"http://arxiv.org/abs/1809.05152v1","url_pdf":"http://arxiv.org/pdf/1809.05152v1.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":"deep-reinforcement-learning-for-event","repo_url":"https://github.com/jj-zhu/resource_aware_control_rl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep 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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}