Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural DynamicsDownload PDF

Anonymous

22 Sept 2022, 12:38 (modified: 17 Nov 2022, 07:00)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: Reinforcement Learning, Neural Network, Representation Learning.
TL;DR: We identify a class of Markov decision processes with neural network parameterization and propose an oracle-efficient algorithm whose sample complexity does not depend on the Eluder dimension of the NN class.
Abstract: Incorporated with the recent advances in deep learning, deep reinforcement learning (DRL) has achieved tremendous success in empirical study. However, analyzing DRL is still challenging due to the complexity of the neural network class. In this paper, we address such a challenge by analyzing the Markov decision process (MDP) with neural dynamics, which covers several existing models as special cases, including the kernelized nonlinear regulator (KNR) model and the linear MDP. We propose a novel algorithm that designs exploration incentives via learnable representations of the dynamics model by embedding the neural dynamics into a kernel space induced by the system noise. We further establish an upper bound on the sample complexity of the algorithm, which demonstrates the sample efficiency of the algorithm. We highlight that, unlike previous analyses of RL algorithms with function approximation, our bound on the sample complexity does not depend on the Eluder dimension of the neural network class, which is known to be exponentially large (Dong et al., 2021).
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Supplementary Material: zip
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
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)
20 Replies

Loading