Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs with Unbounded Costs

ArXi:2603.17875v1 Announce Type: new Marko decision processes (MDPs) is viewed as an optimization of an objective function over certain linear operators over general function spaces. Using the well-established perturbation theory of linear operators, this viewpoint allows one to identify derivatives of the objective function as a function of the linear operators. This leads to generalization of many well-known results in reinforcement learning to cases with generate state and action spaces.