Summary of Efficient Reinforcement Learning For Global Decision Making in the Presence Of Local Agents at Scale, by Emile Anand et al.
Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale
by Emile Anand, Guannan Qu
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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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 proposed SUBSAMPLE-Q algorithm tackles the scalability challenge in reinforcement learning for global decision-making with local agents. In this setting, traditional methods are limited by the exponential growth of the state space with respect to the number of agents. To address this issue, the authors introduce a policy computation mechanism that subsamples k ≤ n local agents, resulting in a time complexity polynomial in k. The learned policy is shown to converge to the optimal policy at a rate of O(1/√k + εk,m), where εk,m represents Bellman noise. The efficacy of SUBSAMPLE-Q is demonstrated through numerical simulations in demand-response and queueing settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed an algorithm called SUBSAMPLE-Q that helps make decisions for many local agents. This can be useful in situations like managing energy use or traffic flow. The problem is that the number of possible states grows very quickly with the number of agents, making it hard to find a good solution. To solve this, they came up with a way to look at only some of the agents and still get close to the best decision. This works because the more agents you look at, the better your decisions will be. They tested their algorithm in two different scenarios and showed that it can make good decisions. |
Keywords
* Artificial intelligence * Reinforcement learning
