## Raisin: Residual Algorithms for Versatile Offline Reinforcement Learning

22 Sept 2022, 12:41 (modified: 18 Nov 2022, 03:57)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: reinforcement learning, offline RL, residual algorithms, residual gradient
Abstract: The residual gradient algorithm (RG), gradient descent of the Mean Squared Bellman Error, brings robust convergence guarantees to bootstrapped value estimation. Meanwhile, the far more common semi-gradient algorithm (SG) suffers from well-known instabilities and divergence. Unfortunately, RG often converges slowly in practice. Baird (1995) proposed residual algorithms (RA), weighted averaging of RG and SG, to combine RG's robust convergence and SG's speed. RA works moderately well in the online setting. We find, however, that RA works disproportionately well in the offline setting. Concretely, we find that merely adding a variable residual component to SAC increases its score on D4RL gym tasks by a median factor of 54. We further show that using the minimum of ten critics lets our algorithm match SAC-$N$'s state-of-the-art returns using 50$\times$ less compute and no additional hyperparameters. In contrast, TD3+BC with the same minimum-of-ten-critics trick does not match SAC-$N$'s returns on a handful of 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.
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)
13 Replies

Loading