arXiv:2510.10544v3 Announce Type: replace Abstract: We derive a novel PAC-Bayesian generalization bound for reinforcement learning that explicitly accounts for Markov dependencies in the data, through the chain's mixing time. This contributes to overcoming challenges in obtaining generalization guarantees for reinforcement learning, where the sequential nature of data breaks the independence assumptions underlying classical bounds. The new bound provides non-vacuous certificates for modern off-policy algorithms such as Soft Actor-Critic. We demonstrate the practical utility of the bound throug
The continuous drive for more robust and generalizable AI models, particularly in reinforcement learning, pushes researchers to address fundamental theoretical challenges.
Improved generalization bounds for reinforcement learning can lead to more reliable, auditable, and deployable AI systems across various applications, reducing the 'sim-to-real' gap.
The ability to formally guarantee performance in complex, sequential decision-making tasks becomes more feasible, potentially accelerating real-world adoption of advanced RL agents.
- · AI developers
- · Robotics industry
- · Autonomous systems
- · Machine learning researchers
- · Developers of unstable RL systems
- · Purely empirical RL approaches
More theoretically grounded reinforcement learning algorithms will emerge, leading to more predictable performance in diverse environments.
This foundational work could accelerate the development and deployment of truly autonomous AI agents capable of complex decision-making in the real world.
Increased reliability and trust in RL systems might facilitate their integration into critical infrastructure and high-stakes applications, potentially impacting regulatory frameworks.
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Read at arXiv cs.LG