Abstract:
Dynamic programming provides a formalism for making near-optimal decisions in sequential settings. A central task in approximate dynamic programming and reinforcement learning is to estimate the solution to the Bellman fixed point equation. Given the large state spaces that arise in practice, function approximation plays an essential role, and the resulting projected Bellman equations...