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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper introduces a framework for learning from options in reinforcement learning. An option is a policy which has some probability of terminating at a certain state. This paper introduces the notion of an "option policy", which is like a high-level policy that allows for multi-step transition between states. They show how to make the option model universal with respect to rewards, and provide an TD-style algorithm for learning with such models.