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Spring 2011
Off-policy reinforcement learning is useful in many contexts. Maei, Sutton, Szepesvari, and others, have recently introduced a new class of algorithms, the most advanced of which is GQ(lambda), for off-policy reinforcement learning. These algorithms are the first stable methods for general...
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Spring 2013
Gradient-TD methods are a new family of learning algorithms that are stable and convergent under a wider range of conditions than previous reinforcement learning algorithms. In particular, gradient-TD algorithms enable off-policy problems---problems where the distribution of the data is different...