Summary of Pps-qmix: Periodically Parameter Sharing For Accelerating Convergence Of Multi-agent Reinforcement Learning, by Ke Zhang et al.
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning
by Ke Zhang, DanDan Zhu, Qiuhan Xu, Hao Zhou, Ce Zheng
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed Average Periodically Parameter Sharing (A-PPS), Reward-Scalability Periodically Parameter Sharing (RS-PPS), and Partial Personalized Periodically Parameter Sharing (PP-PPS) mechanisms aim to accelerate the training process in Multi-Agent Reinforcement Learning (MARL). The current centralized function-based methods suffer from joint errors introduced by other agents, hindering efficient learning. Inspired by federated learning, these novel approaches enable agents to share Q-value networks periodically during training, adapting collected rewards as scalability and updating partial neural networks. Evaluations on various tasks in the StarCraft Multi-Agent Challenge (SMAC) environment demonstrate a significant enhancement of 10-30% over classical MARL methods like QMIX, enabling the winning of previously unsolvable tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers want to make it easier and faster for many agents to work together and learn. Right now, making each agent learn on its own can be slow because they don’t know what others are doing. To fix this, the authors came up with three new ways for agents to share information during training. This sharing helps agents learn from each other and make better decisions. The new methods were tested in a special game environment called StarCraft, where many agents need to work together. The results show that these new methods can help agents learn faster and even solve problems they couldn’t before. |
Keywords
» Artificial intelligence » Federated learning » Reinforcement learning
