Summary of Reaching Consensus in Cooperative Multi-agent Reinforcement Learning with Goal Imagination, by Liangzhou Wang et al.
Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination
by Liangzhou Wang, Kaiwen Zhu, Fengming Zhu, Xinghu Yao, Shujie Zhang, Deheng Ye, Haobo Fu, Qiang Fu, Wei Yang
First submitted to arxiv on: 5 Mar 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 The proposed Multi-agent Goal Imagination (MAGI) framework uses a self-supervised generative model to directly model the distribution of future states, alleviating the “curse of dimensionality” problem. This allows agents to reach consensus on optimal joint actions that maximize the team reward. The MAGI framework guides agents to imagine a common goal that is an achievable state with high value, sampled from the distribution of future states. This efficient consensus mechanism demonstrates superiority in both sample efficiency and performance, as shown by results on Multi-agent Particle-Environments and Google Research Football environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a group of robots working together to achieve a shared goal. To do this efficiently, they need to agree on the best actions to take. Current methods for teaching these robots don’t always work well because they don’t consider how all the robots will work together. In this paper, researchers propose a new way for robots to work together by imagining what they want to achieve as a group. This helps them pick the right actions and avoid mistakes. The results show that this approach works better than others in certain situations. |
Keywords
* Artificial intelligence * Generative model * Self supervised
