Model-based Policy Gradient Reinforcement Learning
Policy gradient methods based on REINFORCE are model-free in the sense that they estimate the gradient using only online experiences executing the current stochastic policy. This is extremely wasteful of training data as well as being computationally inefficient. This paper presents a new model-based policy gradient algorithm that uses training experiences much more efficiently. Our approach constructs a series of incomplete models of the MDP, and then applies these models to compute the policy gradient in closed form. The paper describes an algorithm that alternates between pruning (to remove irrelevant parts of the incomplete MDP model), exploration (to gather training data in the relevant parts of the state space), and gradient ascent search. We show experimental results on several benchmark problems including resource-constrained scheduling. The overall feasibility of this approach depends on whether a sufficiently informative partial model can fit into available memory.