Summary of On Convergence Of Average-reward Q-learning in Weakly Communicating Markov Decision Processes, by Yi Wan et al.
On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes
by Yi Wan, Huizhen Yu, Richard S. Sutton
First submitted to arxiv on: 29 Aug 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 The paper investigates Q-learning algorithms for Markov decision processes (MDPs) based on relative value iteration (RVI), which are model-free stochastic analogues of classical RVI methods. The authors analyze these algorithms under the average-reward criterion, focusing on their applicability to large state space problems. They extend previous work on almost-sure convergence analysis from unichain to weakly communicating MDPs, providing a broader range of applications and richer solution structures. Additionally, they characterize the sets to which RVI Q-learning algorithms converge, showing compactness, connectivity, potential non-convexity, and connection to average-reward optimality equations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how reinforcement learning (RL) can help solve problems in situations where we don’t know exactly what’s going on. It focuses on a type of RL called Q-learning, which is good for big problems because it doesn’t need to know the whole problem ahead of time. The researchers looked at two ways to make this work better: one way is to make sure the algorithm is working right even when things aren’t perfectly predictable, and another way is to group similar problems together so we can solve them more efficiently. |
Keywords
» Artificial intelligence » Reinforcement learning
