{"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/cost-adaptation-for-robust-decentralized","title":"Cost Adaptation for Robust Decentralized Swarm Behaviour","arxiv_id":"1709.07114","date":"2017-09-21","proceeding":null,"authors":["Peter Henderson","Matthew Vertescher","David Meger","Mark Coates"],"abstract":"Decentralized receding horizon control (D-RHC) provides a mechanism for\ncoordination in multi-agent settings without a centralized command center.\nHowever, combining a set of different goals, costs, and constraints to form an\nefficient optimization objective for D-RHC can be difficult. To allay this\nproblem, we use a meta-learning process -- cost adaptation -- which generates\nthe optimization objective for D-RHC to solve based on a set of human-generated\npriors (cost and constraint functions) and an auxiliary heuristic. We use this\nadaptive D-RHC method for control of mesh-networked swarm agents. This\nformulation allows a wide range of tasks to be encoded and can account for\nnetwork delays, heterogeneous capabilities, and increasingly large swarms\nthrough the adaptation mechanism. We leverage the Unity3D game engine to build\na simulator capable of introducing artificial networking failures and delays in\nthe swarm. Using the simulator we validate our method on an example coordinated\nexploration task. We demonstrate that cost adaptation allows for more efficient\nand safer task completion under varying environment conditions and increasingly\nlarge swarm sizes. We release our simulator and code to the community for\nfuture work.","url_abs":"http://arxiv.org/abs/1709.07114v2","url_pdf":"http://arxiv.org/pdf/1709.07114v2.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":"cost-adaptation-for-robust-decentralized","repo_url":"https://github.com/Breakend/SocraticSwarm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"meta-learning","task_name":"Meta-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}