Task decomposition and state abstraction are crucial parts in reinforcement learning. It allows an agent to ignore aspects of its current states that are irrelevant to its current decision, and therefore speeds up dynamic programming and learning. This paper presents the SVI algorithm that uses a dynamic Bayesian network model to construct an influence graph that indicates relationships between state variables. SVI performs state abstraction for each subtask by ignoring irrelevant state variables and lower level subtasks. Experiment results show that the decomposition of tasks introduced by SVI can significantly accelerate constructing a near-optimal policy. This general framework can be applied to a broad spectrum of complex real world problems such as robotics, industrial manufacturing, games and others.