Bayesian Q learning method with Dyna architecture and prioritized sweeping - Jun YU; Quan LIU; Qi-ming FU; Hong-kun SUN; Gui-xing CHEN (Tongxin xuebao)
In order to balance this trade-off, a probability distribution was used in Bayesian Q learning method to de-scribe the uncertainty of the Q value and choose actions with this distribution. But the slow convergence is a big problem for Bayesian Q-Learning. In allusion to the above problems, a novel B ian Q learning algorithm with Dyna architec-ture and prioritized sweeping, called Dyna-PS-BayesQL was proposed. The algorithm mainly includes two parts: in the learning part, it models the transition function and reward function according to collected samples, and update Q value function by Bayesian Q-learning, in the programming part, it updates the Q value function by using prioritized sweeping and dynamic programming methods based on the constructed model, which can improve the efficiency of using the his-torical information. Applying the Dyna-PS-BayesQL to the chain problem and maze navigation problem, the results show that the proposed algorithm can get a good performance of balancing the exploration and exploitation in the learning process, and get a better convergence performance.