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Discovering Options from Example Trajectories
This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
