Vijaykumar Gullapalli

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Acknowledgements Andrew Barto has been a constant source of inspiration to me throughout my tenure as a graduate student. His excellent technical expertise in an incredible range of disciplines makes him unique among researchers and unique as an advisor. I have beneted tremendously from Andy's guidance, enthusiasm, constructive criticism, and(More)
The \forward modeling" approach of Jor-dan and Rumelhart has been shown to be applicable when supervised learning methods are to be used for solving reinforcement learning tasks. Because such tasks are natural candidates for the application of reinforcement learning methods, there is a need to evaluate the relative merits of these two learning methods on(More)
ACKNOWLEDGEMENTS The in BLOCKINuence of Andrew Barto and Michael Jordan on my thinking is evidenced throughout this dissertation. They provided technical training, constructive criticism, guidance, and inspiration throughout my tenure as a graduate student. Andy helped me to discriminate between vital research topics and those of secondary import, and urged(More)
Reinforcement Learning methods based on approximating dynamic programming (DP) are receiving increased attention due to their utility in forming reactive control policies for systems embedded in dynamic environments. Environments are usually modeled as controlled Markov processes, but when the environment model is not known a priori, adaptive methods are(More)
We present a new algorithm for solving Markov decision problems that extends the modiied policy iteration algorithm of Puterman and Shin 6] in two important ways: 1) The new algorithm is asynchronous in that it allows the values of states to be updated in arbitrary order, and it does not need to consider all actions in each state while updating the policy.(More)
In this paper, a peg-in-hole insertion task is used as an example to illustrate the utility of direct as-sociative reinforcement learning methods for learning control under real-world conditions of uncertainty and noise. An associative reinforcement learning system has to learn appropriate actions in various situations through search guided by evaluative(More)
A peg-in-hole insertion task is used as an example to illustrate the utility of direct associative reinforcement learning methods for learning control under real-world conditions of uncertainty and noise. Task complexity due to the use of an unchamfered hole and a clearance of less than 0:2mm is compounded by the presence of positional uncertainty of(More)