This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamic programming-based reinforcement learning method, with the TD() return estimation process, which is typically used in actor-critic learning, another well-known dynamic programming-based reinforcement learning method. The parameter is used to distribute credit throughout sequences of actions, leading ...

A knowledge based segmentation critic algorithm to enhance recognition of courtesy amounts on bank checks is proposed in this paper. This algorithm extracts the context from the handwritten material and uses a syntax parser based on a deterministic finite automaton to provide adequate feedback to enhance recognition. The segmentation critic presented is capable of handling a number of commonly ...

This paper reviews efforts to design an environment for authoring group performance support system (GPSS) agents that can interact with remote internet resources, applications, and users. We review the architecture of the GPSS environment and show how its tutor/critic elements do and don’t map into an idealized specification extracted from the agent oriented programming and inter-agent communic...

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 ...