Summary of Non-asymptotic Analysis For Single-loop (natural) Actor-critic with Compatible Function Approximation, by Yudan Wang et al.
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation
by Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 provides tight non-asymptotic convergence bounds for actor-critic (AC) and natural AC (NAC) algorithms, which are used in reinforcement learning to learn optimal policies. The authors improve upon existing studies by eliminating approximation errors and achieving better sample complexities of O(ε^(-2)) for AC and O(ε^(-3)) for NAC. The paper analyzes the convergence of both algorithms with compatible function approximation, focusing on the single-loop setting with a single Markovian sample trajectory. The authors’ major technical novelty lies in analyzing stochastic bias due to policy-dependent and time-varying function approximation in the critic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how actor-critic algorithms work in reinforcement learning. It provides new insights into how these algorithms learn optimal policies, which is important for many real-world applications like robotics and finance. The authors show that their approach can achieve better results than previous methods, making it a valuable contribution to the field. |
Keywords
» Artificial intelligence » Reinforcement learning
