Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement LearningDownload PDF

Published: 12 Jan 2021, Last Modified: 05 May 2023ICLR 2021 PosterReaders: Everyone
Keywords: deep Q-learning, data-efficient RL, rank-collapse, offline RL
Abstract: We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradient updates decrease the expressivity of the current value network. We char- acterize this loss of expressivity via a drop in the rank of the learned value net- work features, and show that this typically corresponds to a performance drop. We demonstrate this phenomenon on Atari and Gym benchmarks, in both offline and online RL settings. We formally analyze this phenomenon and show that it results from a pathological interaction between bootstrapping and gradient-based optimization. We further show that mitigating implicit under-parameterization by controlling rank collapse can improve performance.
One-sentence Summary: Identifies and studies feature matrix rank collapse (i.e. implicit regularization) in deep Q-learning methods.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Data: [DQN Replay Dataset](
21 Replies