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Despite overparameterization, deep networks trained via supervised learning are surprisingly easy to optimize and exhibit excellent generalization. One hypothesis to explain this is that overparameterized deep networks enjoy the benefits of implicit regularization induced by stochastic gradient descent, which favors parsimonious solutions that generalize well on test inputs. It is reasonable to surmise that deep reinforcement learning (RL) methods could also benefit from this effect. In this paper, we discuss how the implicit regularization effect of SGD seen in supervised learning could in fact be harmful in the offline deep RL setting, leading to poor generalization and degenerate feature representations. Our theoretical analysis shows that when existing models of implicit regularization are applied to temporal difference learning, the resulting derived regularizer favors degenerate solutions with excessive aliasing, in stark contrast to the supervised learning case. We back up these findings empirically, showing that feature representations learned by a deep network value function trained via bootstrapping can indeed become degenerate, aliasing the representations for state-action pairs that appear on either side of the Bellman backup. To address this issue, we derive the form of this implicit regularizer and, inspired by this derivation, propose a simple and effective explicit regularizer, called DR3, that counteracts the undesirable effects of this implicit regularizer. When combined with existing offline RL methods, DR3 substantially improves performance and stability, alleviating unlearning in Atari 2600 games, D4RL domains, and robotic manipulation from images.
Author Information
Aviral Kumar (UC Berkeley)
Rishabh Agarwal (Google Research, Brain Team)
Tengyu Ma (Stanford University)
Aaron Courville (U. Montreal)
George Tucker (Google Brain)
Sergey Levine (UC Berkeley)
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2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
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2016 : Adversarially Learned Inference (ALI) and BiGANs »
Aaron Courville -
2016 Poster: Professor Forcing: A New Algorithm for Training Recurrent Networks »
Alex M Lamb · Anirudh Goyal · Ying Zhang · Saizheng Zhang · Aaron Courville · Yoshua Bengio -
2016 Oral: Matrix Completion has No Spurious Local Minimum »
Rong Ge · Jason Lee · Tengyu Ma -
2016 Poster: Matrix Completion has No Spurious Local Minimum »
Rong Ge · Jason Lee · Tengyu Ma -
2016 Poster: A Non-generative Framework and Convex Relaxations for Unsupervised Learning »
Elad Hazan · Tengyu Ma -
2015 : Introduction »
Aaron Courville -
2015 Workshop: Multimodal Machine Learning »
Louis-Philippe Morency · Tadas Baltrusaitis · Aaron Courville · Kyunghyun Cho -
2015 Poster: Sum-of-Squares Lower Bounds for Sparse PCA »
Tengyu Ma · Avi Wigderson -
2015 Poster: A Recurrent Latent Variable Model for Sequential Data »
Junyoung Chung · Kyle Kastner · Laurent Dinh · Kratarth Goel · Aaron Courville · Yoshua Bengio -
2014 Poster: On Communication Cost of Distributed Statistical Estimation and Dimensionality »
Ankit Garg · Tengyu Ma · Huy Nguyen -
2014 Oral: On Communication Cost of Distributed Statistical Estimation and Dimensionality »
Ankit Garg · Tengyu Ma · Huy Nguyen -
2014 Poster: Generative Adversarial Nets »
Ian Goodfellow · Jean Pouget-Abadie · Mehdi Mirza · Bing Xu · David Warde-Farley · Sherjil Ozair · Aaron Courville · Yoshua Bengio -
2013 Poster: Multi-Prediction Deep Boltzmann Machines »
Ian Goodfellow · Mehdi Mirza · Aaron Courville · Yoshua Bengio -
2011 Poster: On Tracking The Partition Function »
Guillaume Desjardins · Aaron Courville · Yoshua Bengio -
2009 Poster: An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism »
Aaron Courville · Douglas Eck · Yoshua Bengio -
2009 Session: Oral Session 3: Deep Learning and Network Models »
Aaron Courville -
2008 Session: Oral session 11: Attention and Mind »
Aaron Courville -
2007 Spotlight: The rat as particle filter »
Nathaniel D Daw · Aaron Courville -
2007 Poster: The rat as particle filter »
Nathaniel D Daw · Aaron Courville