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pro vyhledávání: '"Shankar, Tanmay"'
Autor:
Shankar, Tanmay, Gupta, Abhinav
In this paper, we address the discovery of robotic options from demonstrations in an unsupervised manner. Specifically, we present a framework to jointly learn low-level control policies and higher-level policies of how to use them from demonstration
Externí odkaz:
http://arxiv.org/abs/2006.16232
We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions o
Externí odkaz:
http://arxiv.org/abs/1806.07822
Autor:
Salman, Hadi, Singhal, Puneet, Shankar, Tanmay, Yin, Peng, Salman, Ali, Paivine, William, Sartoretti, Guillaume, Travers, Matthew, Choset, Howie
Recent literature in the robotics community has focused on learning robot behaviors that abstract out lower-level details of robot control. To fully leverage the efficacy of such behaviors, it is necessary to select and sequence them to achieve a giv
Externí odkaz:
http://arxiv.org/abs/1803.01446
Deep Reinforcement Learning has enabled the learning of policies for complex tasks in partially observable environments, without explicitly learning the underlying model of the tasks. While such model-free methods achieve considerable performance, th
Externí odkaz:
http://arxiv.org/abs/1701.02392