{"type":"ScholarlyArticle","title":"Heuristic Selection of Actions in Multiagent Reinforcement Learning","authors":[{"name":"Reinaldo A. C. Bianchi","slug":"reinaldo-a-c-bianchi","given":"Reinaldo A. C.","family":"Bianchi","affiliation":""},{"name":"Carlos H. C. Ribeiro","slug":"carlos-h-c-ribeiro","given":"Carlos H. C.","family":"Ribeiro","affiliation":""},{"name":"Anna Helena Reali Costa","slug":"anna-helena-reali-costa","given":"Anna Helena Reali","family":"Costa","affiliation":""}],"abstract":"This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the well-known Multiagent Reinforcement Learning algorithm Minimax-Q. A heuristic function that influences the choice of the actions characterises the HAMMQ algorithm. This function is associated with a preference policy that indicates that a certain action must be taken instead of another. A set of empirical evaluations were conducted for the proposed algorithm in a simplified simulator for the robot soccer domain, and experimental results show that even very simple heuristics enhances significantly the performance of the multiagent reinforcement learning algorithm.","doi":null,"url":"https://ijcai.org/proceedings/2007/110","pdf":"https://ijcai.org/proceedings/2007/0110.pdf","markdown":"https://ijcai.org/proceedings/2007/110.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/110","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":110,"pages":{"first":690,"last":694},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}