Real-time dynamic programming (RTDP) solves Markov decision processes (MDPs) when the initial state is restricted. By visiting (and updating) only a fraction of the state space, this approach can be used to solve problems with intractably large state space. In order to improve the performance of RTDP, a variant based on symbolic representation was proposed, named sRTDP. Traditional RTDP approaches work best on problems with sparse transition matrices where they can often efficiently achieve
-convergence without visiting all states; however, on problems with dense transition matrices where most states are reachable in one step, the sRTDP approach shows an advantage over traditional RTDP by up to three orders of magnitude, as we demonstrate in this paper. We also specify a new variant of sRTDP based on BRTDP, named sBRTDP, which converges quickly when compared to RTDP variants, since it does less updating by making a better choice of the next state to be visited.