Vijaykumar Gullapalli

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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)
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)
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