{"type":"ScholarlyArticle","title":"A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior","authors":[{"name":"Trong Nghia Hoang","slug":"trong-nghia-hoang","given":"Trong Nghia","family":"Hoang","affiliation":""},{"name":"Kian Hsiang Low","slug":"kian-hsiang-low","given":"Kian Hsiang","family":"Low","affiliation":""}],"abstract":"Recent advances in Bayesian reinforcement learning (BRL) have shown that Bayes-optimality is theoretically achievable by modeling the environment's latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior. In self-interested multi-agent environments, the transition dynamics are mainly controlled by the other agent's stochastic behavior for which FDM's independence and modeling assumptions do not hold. As a result, FDM does not allow the other agent's behavior to be generalized across different states nor specified using prior domain knowledge. To overcome these practical limitations of FDM, we propose a generalization of BRL to integrate the general class of parametric models and model priors, thus allowing practitioners' domain knowledge to be exploited to produce a fine-grained and compact representation of the other agent's behavior. Empirical evaluation shows that our approach outperforms existing multi-agent reinforcement learning algorithms.","doi":null,"url":"https://ijcai.org/proceedings/2013/209","pdf":"https://ijcai.org/proceedings/2013/0209.pdf","markdown":"https://ijcai.org/proceedings/2013/209.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/209","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":209,"pages":{"first":1394,"last":1399},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}