In this talk, we address the problem of continuous-time reinforcement learning in scenarios where the dynamics follow a stochastic differential equation. When the underlying dynamics remain unknown and we have access only to discrete-time information, how can we effectively conduct policy evaluation? We first highlight that the commonly used Bellman equation (BE) is not always a reliable approximation to the true value function. We then introduce a new bellman equation, PhiBE, which integrates the discrete-time information into a PDE formulation. The new bellman equation offers a more accurate approximation to the true value function, especially in scenarios where the underlying dynamics change slowly. Moreover, we extend PhiBE to higher orders, providing increasingly accurate approximations. We conduct the error analysis for both BE and PhiBE with explicit dependence on the discounted coefficient, the reward and the dynamics. Additionally, we present a model-free algorithm to solve PhiBE when only discrete-time trajectory data is available. Numerical experiments are provided to validate the theoretical guarantees we propose.