Gated Q-learning: Add Off-Policy Bias to Taste

arXiv:2607.28916v1 Announce Type: new Abstract: Multistep credit assignment is critical for sample-efficient reinforcement learning, yet managing off-policy bias in Q-learning remains a fundamental challenge. For 30 years, practitioners have been limited to a binary choice: eliminate the bias at the cost of severely truncated eligibility traces (Watkins' Q($\lambda$)), or ignore the bias to learn faster while injecting detrimental errors into the value estimates (Peng's Q($\lambda$)). Modern off...