Summary of Whittle Index Learning Algorithms For Restless Bandits with Constant Stepsizes, by Vishesh Mittal et al.
Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes
by Vishesh Mittal, Rahul Meshram, Surya Prakash
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); 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 investigates the Whittle index learning algorithm for restless multi-armed bandits, focusing on index learning with Q-learning. The authors first present Q-learning algorithms with exploration policies such as epsilon-greedy, softmax, and epsilon-softmax using constant stepsizes. They then extend this study to index learning for single-armed restless bandit using a two-timescale variant of stochastic approximation. In this algorithm, the index learning scheme is updated on a slower timescale, while Q-learning updates are performed asynchronously with fixed index values. The authors analyze the performance of their algorithms using constant stepsizes and present numerical examples demonstrating that index learning with Q-learning, deep Q-network (DQN) learning, and linear function approximation with state-aggregation method can learn the Whittle index. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machines can make good decisions when faced with multiple choices. It explores an algorithm called the Whittle index learning algorithm, which helps machines learn from experience. The researchers studied different ways to use this algorithm, including one that combines it with another popular technique called Q-learning. They tested their algorithms on various problems and showed that they can be effective in finding good solutions. |
Keywords
* Artificial intelligence * Softmax
