A Fast Q(λ) Algorithm Based on Second-Order TD Error
FU Qi-Ming1,LIU Quan1,2,SUN Hong-Kun1,GAO Long1,LI Jing1,WANG Hui1
1. School of Computer Science and Technology,Soochow University,Suzhou 215006 2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012
Abstract Q(λ) algorithm is a classic model-free-based off policy reinforcement learning with multiple steps which combines the value iteration and stochastic approximation. Aiming at the low efficiency and slow convergence for traditional Q(λ) algorithm,the n-order TD Error is defined from the aspect of the TD Error which is used to the traditional Q(λ) algorithm,and a fast Q(λ) algorithm based on the second-order TD Error (SOE-FQ(λ)) is presented. The algorithm adjusts the Q value with the second-order TD Error and broadcasts the TD Error to the whole state-action space,which speeds up the convergence of the algorithm. In addition,the convergence rate is analyzed,and the number of iteration mainly depends on 11-γ、1ε under the condition of one-step update. Finally,the SOE-FQ(λ) algorithm is used to the random walk and mountain car,and the experimental results show that the algorithm has the faster convergence rate and better convergence performance.
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2013, Vol. 26 
