Summary of Maidcrl: Semi-centralized Multi-agent Influence Dense-cnn Reinforcement Learning, by Ayesha Siddika Nipu et al.
MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning
by Ayesha Siddika Nipu, Siming Liu, Anthony Harris
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a semi-centralized reinforcement learning algorithm, MAIDCRL, for distributed decision-making in multi-agent systems. The algorithm combines convolutional layers with Dense Reinforcement Learning (MAIDRL) and incorporates agent influence maps to learn effective control on StarCraft Multi-Agent Challenge (SMAC) scenarios. The authors evaluate the performance of MAIDCRL on both homogeneous and heterogeneous scenarios, showing significant improvements over existing methods, particularly in more complex heterogeneous scenarios. The paper also investigates the stability and robustness of the model, demonstrating its ability to achieve higher winning rates and accelerate the learning process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can make decisions together. Right now, this is a big challenge because it’s hard for them to work out what each other is doing. The researchers came up with an idea called MAIDCRL that uses pictures (like what you see on a computer screen) and special math to help the computers learn. They tested their idea on different scenarios and found that it worked really well, especially when things got complicated. This means we might be able to use this technology to create more intelligent robots or even self-driving cars. |
Keywords
* Artificial intelligence * Reinforcement learning
