Please use this identifier to cite or link to this item:
|dc.title||A general framework for interacting Bayes-optimally with self-interested agents using arbitrary parametric model and model prior|
|dc.identifier.citation||Hoang, T.N.,Low, K.H. (2013). A general framework for interacting Bayes-optimally with self-interested agents using arbitrary parametric model and model prior. IJCAI International Joint Conference on Artificial Intelligence : 1394-1400. ScholarBank@NUS Repository.|
|dc.description.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 multiagent 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.|
|dc.description.sourcetitle||IJCAI International Joint Conference on Artificial Intelligence|
|Appears in Collections:||Staff Publications|
Show simple item record
Files in This Item:
There are no files associated with this item.
checked on Nov 24, 2022
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.