Summary of Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning Via Equivariance, by Joshua Mcclellan et al.
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
by Joshua McClellan, Naveed Haghani, John Winder, Furong Huang, Pratap Tokekar
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Exploration-enhanced Equivariant Graph Neural Networks (E2GN2), which combines the benefits of Equivariant Graph Neural Networks (EGNN) with exploration techniques to improve sample efficiency and generalization in Multi-Agent Reinforcement Learning (MARL). EGNNs have been shown to improve learning efficiency and decrease error, but naive applications can lead to poor early exploration. The authors demonstrate that E2GN2 outperforms standard GNNs in MARL benchmarks MPE and SMACv2, achieving a 2x-5x gain in sample efficiency and final reward convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer networks called neural networks to help robots work together better. It’s like training a team of robots to do tasks together, but the problem is that these networks don’t learn as well when there are many robots. The researchers found a way to make the networks more efficient and able to learn from fewer experiences. They also created a new type of network called E2GN2, which helps robots explore their environment better. This means they can find good solutions faster and be more successful in tasks. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
