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Convergence in Off-Policy Settings
A Brief Convergence Analysis of Existing Algorithms in the Literature
⚠️ Under construction!
Off-policy reinforcement learning (RL) presents a challenging yet essential setting for the practical application of RL algorithms in real-world scenarios. In this post, we provide a concise analysis of existing algorithms in both off-policy and offline settings.
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Optimization to Reinforcement Learning
An Optimization Perspective on Reinforcement Learning (RL) for Algorithm Design
⚠️ Under review!
Drawing inspiration from the correspondence between optimization and Markov Decision Processes (MDPs), we explore extensions of existing optimization algorithms to RL, employing a mathematically rigorous approach. In particular, we investigate Quasi-Newton methods and propose a novel RL algorithm that demonstrates convergence in both model-based and model-free settings, achieving memory and computational complexity.