Renewal, resurgence, and alternative reinforcement context

Effects of differential rates of alternative reinforcement on resurgence of human behavior.

Despite the success of exposure-based psychotherapies in anxiety treatment, relapse remains problematic. Resurgence, the return of previously eliminated behavior following the elimination of an alternative source of reinforcement, is a promising model of operant relapse. Nonhuman resurgence research has shown that higher rates of alternative reinforcement result in faster, more comprehensive su...

Renewal Monte Carlo: Renewal theory based reinforcement learning

In this paper, we present an online reinforcement learning algorithm, called Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm and retains the advantages of Monte Carlo methods including low bias, simplicity, and ease of implementation while, at the same time, circumvents their key drawbacks of high variance a...

A Quantitative Analysis of Response Elimination and Resurgence Using Rich, Lean, and Thinning Schedules of Alternative Reinforcement

iCORE Research Grant Renewal Proposal Reinforcement Learning and Artificial Intelligence

The RLAI research program pursues an approach to artificial intelligence and engineering problems in which they are formulated as large optimal control problems and approximately solved using reinforcement learning methods. Reinforcement learning is a new body of theory and techniques for optimal control that has been developed in the last twenty years primarily within the machine learning and ...

An evaluation of generalization, renewal, and resurgence during functional communication training conducted via telehealth