Summary of Stochastic Approximation with Delayed Updates: Finite-time Rates Under Markovian Sampling, by Arman Adibi et al.
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
by Arman Adibi, Nicolo Dal Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Systems and Control (eess.SY); Optimization and Control (math.OC)
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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 This paper explores the performance of stochastic approximation (SA) schemes with delayed updates in large-scale and multi-agent reinforcement learning applications. The authors study how time-varying bounded delays interact with the underlying Markov process to affect the finite-time performance of SA, which is crucial for optimization problems. They develop a novel proof technique that establishes uniform boundedness of the iterates, leading to a tight bound on the convergence rate in terms of both the maximum delay and mixing time. The authors also propose a delay-adaptive SA scheme that can mitigate the impact of delays without prior knowledge of the delay sequence. This work provides valuable insights into the finite-time effects of delays for various algorithms, including TD learning, Q-learning, and stochastic gradient descent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stochastic approximation is an important technique in machine learning, but it can be slow when there are delays. In this paper, researchers study how to speed up SA by understanding how delays affect its performance. They develop new methods to analyze SA with delays and show that some techniques are better than others at handling delays. This helps us design more efficient algorithms for big problems like learning from multiple agents or large datasets. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Reinforcement learning * Stochastic gradient descent
