New algorithm tackles variance in reinforcement learning policy evaluation

Researchers have developed a new double-loop gradient-based algorithm to address high variance in reinforcement learning policy evaluation. This algorithm aims to learn data-collecting policies that are robust to uncertainties in transition functions, a common issue when real-world environments differ from simulation models. The proposed method demonstrates reduced sensitivity to transition perturbations compared to existing approaches, with theoretical guarantees for global convergence. AI

IMPACT This research could lead to more reliable and efficient policy evaluation in reinforcement learning, reducing the need for costly real-world data collection.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new algorithm for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Behavior Policy Search
• Double-loop Gradient-based Algorithm
• Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems
• reinforcement learning
• Transition functions of decomposed signals
• Transition Perturbations

• paper
• other

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