Hierarchical Reinforcement Learning with MAXQ Method
Reinforcement learning is an important branch of machine learning.In the system of reinforcement learning,the learning stategies increase exponentially along with the number of state variables,which is called "dimensions disaster".Here a hierarchical reinforcement learning based on the MAXQ is proposed to solve this problem,which is realized by decomposing the task to different level,thus sub tasks in every level can be solved in relatively smaller scale.This method turns out to be effective to decrease the stategies.Finally,offer the concerned algorithm MAXQ RLA.