Knowledge Transfer has gradually became a research hot pot in machine learning

which tries to transfer the knowledge from the historical tasks to the target task in order to speed up the convergence rate and improve the performance of algorithms.With respect to the slow convergence rate of traditional reinforcement learning algorithms

this paper proposed to transfer the value function between different similar learning tasks with the same state space and action space

which tries to reduce the needed samples in the target task and speed up the convergence rate.Based on the framework of on-policy Sarsa algorithm

combined with the value function transfer method

this paper put forward a novel fast Sarsa algorithm based on the value function transferVFT-Sarsa.At the beginning

the algorithm uses Bisimulation metric to measure the distance between states in target task and historical task on the condition that these tasks have the same state space and action space

transfers the value function if the distance meets some condition

and finally executes the learning algorithm.At the end

apply the proposed algorithm in Random Walk

compared with Sarsa algorithm

Q-Learning and QV algorithm

the results show that the proposed algorithm can get a better convergence rate with a good performance.