Summary of Order-optimal Global Convergence For Average Reward Reinforcement Learning Via Actor-critic Approach, by Swetha Ganesh et al.
Order-Optimal Global Convergence for Average Reward Reinforcement Learning via Actor-Critic Approach
by Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new algorithm for average-reward reinforcement learning with general parametrization, addressing suboptimal state-of-the-art (SOTA) guarantees that require prior knowledge of the mixing time. The Multi-level Monte Carlo-based Natural Actor-Critic (MLMC-NAC) algorithm achieves a global convergence rate of (1/) without needing this knowledge, surpassing the SOTA bound of (T^{-1/4}). This work is significant for practical scenarios where mixing time information is unavailable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better in situations where we don’t know everything about what’s happening. It develops a new way to teach machines to make good choices when the situation changes, even if we can’t predict exactly how it will change. The new method is faster and more reliable than previous approaches and could be used in many real-world applications. |
Keywords
* Artificial intelligence * Reinforcement learning
