{"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/efficient-evolutionary-methods-for-game-agent","title":"Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best","arxiv_id":"1901.00723","date":"2019-01-03","proceeding":null,"authors":["Simon M. Lucas","Jialin Liu","Ivan Bravi","Raluca D. Gaina","John Woodward","Vanessa Volz","Diego Perez-Liebana"],"abstract":"This paper introduces a simple and fast variant of Planet Wars as a test-bed\nfor statistical planning based Game AI agents, and for noisy hyper-parameter\noptimisation. Planet Wars is a real-time strategy game with simple rules but\ncomplex game-play. The variant introduced in this paper is designed for speed\nto enable efficient experimentation, and also for a fixed action space to\nenable practical inter-operability with General Video Game AI agents. If we\ntreat the game as a win-loss game (which is standard), then this leads to\nchallenging noisy optimisation problems both in tuning agents to play the game,\nand in tuning game parameters. Here we focus on the problem of tuning an agent,\nand report results using the recently developed N-Tuple Bandit Evolutionary\nAlgorithm and a number of other optimisers, including Sequential Model-based\nAlgorithm Configuration (SMAC). Results indicate that the N-Tuple Bandit\nEvolutionary offers competitive performance as well as insight into the effects\nof combinations of parameter choices.","url_abs":"http://arxiv.org/abs/1901.00723v1","url_pdf":"http://arxiv.org/pdf/1901.00723v1.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":"efficient-evolutionary-methods-for-game-agent","repo_url":"https://github.com/SimonLucas/ntbea","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"smac","task_name":"SMAC"},{"task_slug":"smac-1","task_name":"SMAC+"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}