A General Framework for Interacting Bayes-Optimally with Self-Interested Agents Using Arbitrary Parametric Model and Model Prior
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.
Trong Nghia Hoang Kian Hsiang Low
Department of Computer Science,National University of Singapore Republic of Singapore
国际会议
北京
英文
1394-1400
2013-08-01（万方平台首次上网日期，不代表论文的发表时间）