Several New Advances of Logical Markov Decision Processes and Relational Markov Decision Processes
逻辑马尔可夫决策编程和关系马尔可夫决策编程若干最新进展

Using logical Markov decision processes (LDMDPs) and relational Markov decision processes (RMDPs) one can compactly and declaratively represent complex Markov decision processes.This paper firstly introduces central con- cepts of LOMDPs and RMDPs.Then several algorithms that are different from regular Markov decision processes are reviewed:1.The transition method relying on ground state space.2.A relational upgrade of the Bellman update oper- ation.3.Approximate policy iteration using policy bias space.Finally,the paper gives conclusions of recent work and suggests future work.In this way,people are able to get an intensive,comprehensive and in-depth understanding of LOMDPs and RMDPs.