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Long-Term Credit Assignment via Model-based Temporal Shortcuts
Michel Ma · Pierluca D'Oro · Yoshua Bengio · Pierre-Luc Bacon
Event URL: https://openreview.net/forum?id=doy35IAGewq »

This work explores the question of long-term credit assignment in reinforcement learning. Assigning credit over long distances has historically been difficult in both reinforcement learning and recurrent neural networks, where discounting or gradient truncation respectively are often necessary for feasibility, but limit the model's ability to reason over longer time scales. We propose LVGTS, a novel model-based algorithm that bridges the gap between the two fields. By using backpropagation through a latent model and temporal shortcuts to directly propagate gradients, LVGTS assigns credit from the future to the possibly distant past regardless of the use of discounting or gradient truncation. We show, on simple but carefully-designed problems, that our approach is able to perform effective credit assignment even in the presence of distractions.

Author Information

Michel Ma (University of Montreal)
Pierluca D'Oro (Mila)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.

Pierre-Luc Bacon (McGill University)

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