New Q-learning theory offers tighter convergence rate analysis

Researchers have developed a novel theoretical framework for analyzing Q-learning, a fundamental algorithm in reinforcement learning. This new approach views Q-learning through the lens of switching systems, deriving a direct stochastic representation of the Q-learning error. The analysis yields convergence rates expressed through the joint spectral radius of a direct switching family, offering tighter bounds than previous methods. AI

影响 Introduces a new theoretical framework for analyzing Q-learning convergence, potentially leading to more robust reinforcement learning agents.

排序理由 This is a theoretical computer science paper published on arXiv, detailing a new analytical framework for a reinforcement learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Donghwan Lee
• Q-learning

• 论文
• 其他

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