Unsupervised Model-based Pre-training for Data-efficient Control from PixelsDownload PDF

Anonymous

22 Sept 2022, 12:39 (modified: 19 Nov 2022, 03:14)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: unsupervised reinforcement learning, world models, planning
TL;DR: We combine the findings of a large-scale study on the Unsupervised RL Benchmark and propose a new hybrid planner, to establish an end-to-end approach that can be efficiently fine-tuned after unsupervised pre-training.
Abstract: Controlling artificial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed in this but require large amounts of interactions between the agent and the environment. To alleviate the issue, unsupervised RL proposes to employ self-supervised interaction and learning, for adapting faster to future tasks. Yet, whether current unsupervised strategies improve generalization capabilities is still unclear, especially in visual control settings. In this work, we design an unsupervised RL strategy for data-efficient visual control. First, we show that world models pre-trained with data collected using unsupervised RL can facilitate adaptation for future tasks. Then, we analyze several design choices to adapt faster, effectively reusing the agents' pre-trained components, and planning in imagination, with our hybrid planner, which we dub Dyna-MPC. By combining the findings of a large-scale empirical study, we establish an approach that strongly improves performance on the Unsupervised RL Benchmark, requiring 20$\times$ less data to match the performance of supervised methods. The approach also demonstrates robust performance on the Real-Word RL benchmark, hinting that the approach generalizes to noisy environments.
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)
17 Replies

Loading