Summary of Asynchronous Stochastic Approximation and Average-reward Reinforcement Learning, by Huizhen Yu et al.
Asynchronous Stochastic Approximation and Average-Reward Reinforcement Learning
by Huizhen Yu, Yi Wan, Richard S. Sutton
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Asynchronous stochastic approximation (SA) algorithms are applied to reinforcement learning in semi-Markov decision processes (SMDPs) with an average-reward criterion. The authors extend a stability proof method for more general noise conditions, leading to broader convergence guarantees for asynchronous SA algorithms. They then establish the convergence of an asynchronous SA analogue of Schweitzer’s classical relative value iteration algorithm, RVI Q-learning, for finite-space, weakly communicating SMDPs. Additionally, they introduce new monotonicity conditions for estimating the optimal reward rate in RVI Q-learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to improve reinforcement learning in complex decision-making situations. Researchers use special algorithms that adjust quickly and accurately to changing conditions. They develop new methods to ensure these algorithms work well even with noisy data. This helps them learn better strategies for solving problems like finding the best route or managing resources. |
Keywords
» Artificial intelligence » Reinforcement learning
