In this talk, we will review some recent advances in the area of multistage decision making under uncertainty, especially in the domain of stochastic and robust optimization. We will present some new algorithmic development that allows for exactly solving huge-scale stochastic programs with integer recourse decisions, and algorithms in a dual perspective that can deal with infeasibility in problems. This significantly extends the scope of stochastic dual dynamic programming (SDDP) algorithms from convex or binary state variable cases to general nonconvex problems. We will also present a new analysis of the iteration complexity of the proposed algorithms. This settles some open questions in regards of the complexity of SDDP.