Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
arXiv:2603.08287v2 Announce Type: replace-cross Abstract: We analyze the Bayesian regret of the Gaussian process posterior sampling reinforcement learning (GP-PSRL) algorithm. Posterior sampling is a heuristic for decision-making under uncertainty that has been used to develop successful algorithms for a variety of continuous control problems. However, theoretical work on GP-PSRL is limited. All known regret bounds either have a sub-optimal growth rate, require strong smoothness assumptions, or fail to properly account for the fact that the set of possible system states is unbounded. Through a
This paper addresses a foundational theoretical gap in posterior sampling reinforcement learning, advancing the mathematical understanding of continuous control systems, which is critical for robust AI development.
Improved theoretical guarantees for reinforcement learning in complex, unbounded environments are crucial for developing more reliable and autonomous AI systems, impacting fields from robotics to advanced control.
The robust theoretical framework proposed for GP-PSRL provides a new basis for designing and analyzing reinforcement learning algorithms, particularly in real-world scenarios with continuous and expansive state spaces.
- · AI researchers
- · Robotics developers
- · Autonomous systems sector
- · Academic institutions
- · Algorithms lacking strong theoretical guarantees
Improved performance and reliability of AI systems utilizing posterior sampling reinforcement learning.
Accelerated development of AI agents capable of operating in highly complex and dynamic environments.
Enhanced trust in autonomous decision-making systems across critical infrastructure and high-stakes applications.
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