Value-gradient learning

We describe an Adaptive Dynamic Programming algorithm VGL(λ) for learning a critic function over a large continuous state space. The algorithm, which requires a learned model of the environment, extends Dual Heuristic Dynamic Programming to include a bootstrapping parameter analogous to that used in the reinforcement learning algorithm TD(λ). We provide on-line and batch mode implementations of the algorithm, and summarise the theoretical relationships and motivations of using this method over its precursor algorithms Dual Heuristic Dynamic Programming and TD(λ). Experiments for control problems using a neural network and greedy policy are provided.