Heavy-Ball Q-Learning with Residual Weighting Correction

2026-06-25 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

A new corrected heavy-ball Q-learning method for reinforcement learning (RL) is introduced, demonstrating theoretical convergence and identifying specific conditions under which it achieves faster convergence compared to standard Q-learning. This construction is further extended to Q-learning algorithms employing linear function approximation, where similar convergence and acceleration properties are derived. The underlying analysis leverages a switched linear system (SLS) representation of Q-learning algorithms, alongside the joint spectral radius (JSR) of their associated switching families. This SLS perspective offers a novel analytical framework, providing fresh insights into how heavy-ball momentum effectively accelerates the Q-learning process, a viewpoint not typically utilized in conventional Q-learning analyses.

Key takeaway

For machine learning engineers optimizing reinforcement learning algorithms, this research suggests that incorporating heavy-ball momentum, specifically the corrected Heavy-Ball Q-Learning with Residual Weighting Correction, can significantly accelerate convergence. If you are working with Q-learning or its linear function approximation variants, you should investigate the conditions under which this method guarantees faster performance. This offers a theoretical basis for improving training efficiency in your RL deployments.

Key insights

Heavy-Ball Q-Learning with Residual Weighting Correction accelerates RL convergence, analyzed via switched linear systems.

Principles

• Heavy-ball momentum can accelerate Q-learning.
• Switched linear systems offer new RL analysis insights.

Topics

• Reinforcement Learning
• Q-learning
• Heavy-Ball Momentum
• Convergence Analysis
• Switched Linear Systems
• Linear Function Approximation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.