Heavy-Ball Q-Learning with Residual Weighting Correction

arXiv:2606.27112v1 Announce Type: new 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

Source: arXiv cs.LG — read the full report at the original publisher.

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