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
The continuous advancements in AI research necessitate improved learning algorithms to enhance efficiency and accelerate development.
Improved Q-learning methods can significantly accelerate AI training, leading to more complex and faster-deploying AI systems.
Reinforcement learning systems could become more robust and converge much faster, reducing computational demands for effective training.
- · AI researchers
- · Reinforcement learning platforms
- · AI-driven automation
- · Inefficient AI training methods
- · High-compute-cost AI development
Faster and more reliable Q-learning accelerates the development of advanced AI applications.
Reduced training times and computational costs could democratize access to sophisticated reinforcement learning development.
This could lead to breakthroughs in areas requiring extensive trial-and-error learning, such as robotics and autonomous systems.
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Read at arXiv cs.LG