Summary of Gino-q: Learning An Asymptotically Optimal Index Policy For Restless Multi-armed Bandits, by Gongpu Chen et al.
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits
by Gongpu Chen, Soung Chang Liew, Deniz Gunduz
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 restless multi-armed bandit (RMAB) framework is a widely applicable model with various applications. However, its solution is limited by the exponentially growing state space and combinatorial action space, making traditional reinforcement learning methods infeasible for large-scale instances. This paper proposes GINO-Q, a three-timescale stochastic approximation algorithm designed to learn an asymptotically optimal index policy for RMABs. GINO-Q mitigates the curse of dimensionality by decomposing the RMAB into subproblems with the same dimension as a single arm, ensuring that complexity increases linearly with the number of arms. Unlike Whittle-index-based algorithms, GINO-Q does not require RMABs to be indexable, enhancing its flexibility and applicability. Experimental results demonstrate that GINO-Q consistently learns near-optimal policies for non-indexable RMABs where Whittle-index-based algorithms perform poorly, and it converges significantly faster than existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GINO-Q is a new algorithm that helps with big decisions by breaking down complex problems into smaller ones. This makes it possible to solve really hard problems that have many options or actions. The old way of doing this was too slow for large problems, but GINO-Q is much faster and works better even when the problem isn’t easy to solve. It’s like having a superpower to make good choices! |
Keywords
» Artificial intelligence » Reinforcement learning
