A nonlinear control system comprising a network of networks is taught by the use of a two-phase learning procedure realized through novel training techniques and an adaptive critic design. The neural network controller is trained algebraically, offline, by the observation that its gradients must equal corresponding linear gain matrices at chosen operating points. Online learning by a dual heuri...

We prove the convergence of four new reinforcement learning algorithms based on the actorcritic architecture, on function approximation, and on natural gradients. Reinforcement learning is a class of methods for solving Markov decision processes from sample trajectories under lack of model information. Actor-critic reinforcement learning methods are online approximations to policy iteration in ...

Programming robot behavior can be painstaking: for a layperson, this path is unavailable without investing significant effort in building up proficiency in coding. In contrast, nearly half of American households have a pet dog and at least some exposure to animal training, suggesting an alternative path for customizing robot behavior. Unfortunately, most existing reinforcement-learning (RL) alg...

Editor’s Summary: This chapter provides an overview of model-based adaptive critic designs, including background, general algorithms, implementations, and comparisons. The authors begin by introducing the mathematical background of model-reference adaptive critic designs. Various ADP designs such as Heuristic Dynamic Programming (HDP), Dual HDP (DHP), Globalized DHP (GDHP), and Action-Dependent...