Heavy-Ball Q-Learning with Residual Weighting Correction
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Computer Science > Machine Learning
Title:Heavy-Ball Q-Learning with Residual Weighting Correction
Abstract:This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence. It also identifies conditions under which the method is theoretically guaranteed to converge faster than standard Q-learning. The same construction is then extended to Q-learning with linear function approximation, where analogous convergence and acceleration statements are derived. The analysis is based on a switched linear system (SLS) representation of Q-learning algorithms and on the joint spectral radius (JSR) of the associated switching families. This SLS viewpoint is not commonly used in standard analyses of Q-learning, and it provides a complementary framework and new insight into how heavy-ball momentum can accelerate Q-learning.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.27112 [cs.LG] |
| (or arXiv:2606.27112v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27112
arXiv-issued DOI via DataCite (pending registration)
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