Algorithm of TD(λ) Based on Factored Representation
Xin Zhang · Jisuanji gongcheng · 2009
This paper proposes a new algorithm of TD(λ) based on factored representation.The main principle of the algorithm is that states are factored representation, and makes use of Dynamic Bayesian Networks(DBNs) to represent the conditional probability distributions in Markov Decision Processes(MDPs), together with decision-trees representation of value function in the algorithm of TD(λ) to lower the state space exploration and computation complexity.Therefore the algorithm is a promise for solving large-scale MDPs problems which are of a huge state space.Experiments demonstrates the validity of this representation method.