Discovering Options from Example Trajectories

author: Peng Zang, College of Computing, Georgia Institute of Technology
published: Aug. 26, 2009,   recorded: June 2009,   views: 2970


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We present a novel technique for automated problem decomposition to address the problem of scalability in Reinforcement Learning. Our technique makes use of a set of near-optimal trajectories to discover {\it options} and incorporates them into the learning process, dramatically reducing the time it takes to solve the underlying problem. We run a series of experiments in two different domains and show that our method offers up to 30 fold speedup over the baseline.

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