New bounds established for constrained average-reward MDPs

Researchers have established near-optimal sample complexity bounds for constrained average-reward Markov decision processes (CAMDPs) under a generative model. The proposed model-based algorithm achieves sample complexities of $\tilde{O}(\frac{S A (B+H)}{ \epsilon^2})$ for relaxed feasibility and $\tilde{O}(\frac{S A (B+H)}{ \epsilon^2 \zeta^2})$ for strict feasibility. A matching lower bound of $\tilde{\Omega}(\frac{S A (B+H)}{ \epsilon^2\zeta^2})$ was also proven for the strict feasibility case, providing the first minimax-optimal bounds for CAMDPs and closing a theoretical gap. AI

IMPACT Establishes theoretical foundations for decision-making under constraints, potentially impacting AI agents in complex environments.

RANK_REASON Academic paper detailing theoretical bounds for a specific type of Markov decision process. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• alphaXiv
• CatalyzeX Code Finder for Papers
• Constrained average-reward MDPs
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv Recommender
• Influence Flower
• Markov decision processes
• ScienceCast
• Yukuan Wei

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →