Most algorithms are based on policy evaluation in reinforcement learning.The Gaussian process temporal difference is an algorithm that uses Bayesian solution to evaluate value functions.In the method

Gaussian process builds a probabilistic generative model between the immediate reward and the value function through Bellman Equation and Bayesian rule.In order to improve the efficiency of the algorithm

approximate linear approximation for new samples is solved by on-line kernel sparse and least squares in state space.However

the time complexity is still high.To deal with this problem

a Bayesian temporal difference algorithm bases on random projection algorithm is proposed.The elements in dictionary state set are mapped to hash values by hash function.According to the hash values

groups are divided and the comparison between the states is reduced.The experimental results show that this algorithm not only improves the execution speed

but also obtains balance between execution time and precision of the state value function.