A new algorithm for discovery and learning of predictive state representations in dynamical systems without reset is proposed.With proving that any landmark can be used as the initial state

the discovered landmarks are used to identify the history at any time step in a continues data

then the conditional probability of any test at any history is estimated using Monte Carlo approaches

which efficiently solves the difficult problem of obtaining the conditional probability in dynamical systems without reset

thereby it is straightforward to discover and learn predictive state representations.The empirical results show that in case of the obtained predictive state representations’s prediction quality

our algorithm has better prediction accuracy than the suffix-history algorithm

which proves the effectiveness of the proposed algorithm.