arXiv:2606.29806v1 Announce Type: new Abstract: Action-values are foundational to many control algorithms such as Q-learning. Therefore learning action-values efficiently is central to reinforcement learning (RL). However, learning them can be slow, requiring many updates to move values from their initialization, typically near zero, to their true values, which may be far from zero. Moreover, action-value learning algorithms typically update each state-action pair independently, without learning shared value structure across actions within a state. In this paper, we address these inefficiencie
The continuous drive for more efficient and robust reinforcement learning algorithms, especially as AI systems become more complex and autonomous, pushes research towards fundamental improvements.
Efficient Q-learning is crucial for the development of adaptive and sophisticated AI agents, impacting their ability to learn and perform complex tasks with fewer resources and less time.
This research proposes a method to accelerate Q-learning by sharing value structures across actions, potentially reducing the computational cost and training time required for advanced RL applications.
- · AI researchers
- · Reinforcement learning developers
- · Companies deploying autonomous AI agents
- · Robotics companies
- · Developers reliant on traditional, less efficient Q-learning methods
Faster and more efficient training of Q-learning models will be possible.
This could lead to a quicker deployment of advanced AI agents in various applications, from industry to consumer products.
Increased efficiency in RL could democratize access to advanced AI development, as less computational power becomes a bottleneck.
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Read at arXiv cs.LG