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...

This article presents a reinforcement learning framework for continuous-time dynamical systems without a priori discretization of time, state, and action. Based on the Hamilton-Jacobi-Bellman (HJB) equation for infinite-horizon, discounted reward problems, we derive algorithms for estimating value functions and improving policies with the use of function approximators. The process of value func...

A Boundedness Theoretical Analysis for GrADPDesign: A Case Study on Maze Navigation Report Title A new theoretical analysis towards the goal representation adaptive dynamic programming (GrADP) design proposed in [1], [2] is investigated in this paper. Unlike the proofs of convergence for adaptive dynamic programming (ADP) in literature, here we provide a new insight for the error bound between ...

In this paper we describe how an actor critic rein forcement learning agent in a non Markovian domain nds an optimal sequence of actions in a totally model free fashion that is the agent neither learns transitional probabilities and associated rewards nor by how much the state space should be augmented so that the Markov prop erty holds In particular we employ an Elman type re current neural ne...