Pierre-Luc Bacon

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Temporal abstraction plays a key role in scaling up reinforcement learning algorithms. While learning and planning with given temporally extended actions has been well studied , the topic of how to construct this type of abstraction automatically from data is still open. We propose to use the label propagation algorithm for community detection in order to(More)
Deep learning has become the state-of-art tool in many applications, but the evaluation and training of deep models can be time-consuming and computationally expensive. The conditional computation approach has been proposed to tackle this problem (Bengio et al., 2013; Davis & Arel, 2013). It operates by selectively activating only parts of the network at a(More)
Deep learning has become the state-of-art tool in many applications, but the evaluation and training of such models is very time-consuming and expensive. Dropout has been used in order to make the computations sparse (by not involving all units), as well as to regularize the models. In typical dropout, nodes are dropped uniformly at random. Our goal is to(More)
We consider the problem of learning and planning in Markov decision processes with temporally extended actions represented in the options framework. We propose to use predictions about the duration of extended actions to represent the state and show that this leads to a compact pre-dictive state representation model independent of the set of primitive(More)