arXiv:2606.20357v1 Announce Type: new Abstract: We analyze the variance of temporal difference (TD) learning using the phased setting with tabular representation, and show that one of the mechanisms behind its ability to reduce variance is by effectively aggregating over a larger number of independent trajectories. Based on this insight, we demonstrate that (1) the variance of TD is asymptotically bounded from above by Monte Carlo (MC) estimators, and (2) shorter horizon updates incurs less variance for a fixed number of samples. Beyond TD, we show that Direct Advantage Estimation (DAE), a met
The continuous drive for more efficient and robust reinforcement learning algorithms pushes research into fundamental variance reduction techniques like these. Advances in computational power allow for more complex analysis of TD learning variance.
Improved understanding and reduction of variance in Temporal Difference (TD) learning can lead to more stable, efficient, and reliable AI agents, accelerating their deployment and capabilities. This research provides theoretical foundations for practical algorithmic improvements.
The theoretical proof that TD variance is bounded above by Monte Carlo and that shorter horizons reduce variance offers concrete guidance for algorithm design, potentially leading to faster training and better performance for reinforcement learning systems.
- · AI/ML researchers
- · Reinforcement learning practitioners
- · Companies developing AI agents
- · Autonomous systems developers
- · Inefficient RL algorithms
- · Trial-and-error approach to RL tuning
More stable and faster-converging reinforcement learning algorithms become widely available.
This improved stability accelerates the development and commercialization of complex AI agents across various domains.
The enhanced capability of AI agents could lead to new applications and further automation in white-collar and industrial sectors.
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Read at arXiv cs.LG