Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks

An on-line reinforcement learning system that adapts to environmental changes using a mixture of Bayesian networks is described. Building intelligent systems able to adapt to dynamic environments is important for deploying real-world applications. Machine learning approaches, such as those using reinforcement learning methods and stochastic models, have been used to acquire behavior appropriate to environments characterized by uncertainty. However, efficient hybrid architectures based on these approaches have not yet been developed. The results of several experiments demonstrated that an agent using the proposed system can flexibly adapt to various kinds of environmental changes.