Gaussian-Mixture-Model Q-Functions for Policy Iteration in Reinforcement Learning
Published:Dec 21, 2025 15:00
•1 min read
•ArXiv
Analysis
This article likely presents a novel approach to reinforcement learning, focusing on improving the Q-function representation using Gaussian Mixture Models (GMMs). This could lead to more efficient and accurate policy iteration, potentially improving performance in complex environments. The use of GMMs suggests a focus on modeling the uncertainty inherent in reinforcement learning.
Key Takeaways
Reference
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