Summary of Fm3q: Factorized Multi-agent Minimax Q-learning For Two-team Zero-sum Markov Game, by Guangzheng Hu et al.
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game
by Guangzheng Hu, Yuanheng Zhu, Haoran Li, Dongbin Zhao
First submitted to arxiv on: 1 Feb 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 individual-global-minimax (IGMM) principle ensures coherence between two-team minimax behaviors and individual greedy behaviors in zero-sum Markov games. A novel multi-agent reinforcement learning framework, Factorized Multi-Agent MiniMax Q-Learning (FM3Q), is presented to factorize the joint minimax Q function into individual ones and iteratively solve for IGMM-satisfied minimax Q functions. An online learning algorithm with neural networks implements FM3Q and obtains deterministic and decentralized minimax policies for two-team players. Theoretical analysis proves the convergence of FM3Q, which outperforms existing methods in three evaluated environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Two teams play a game where each team gets a reward if they win, and a penalty if they lose. Players need to decide what actions to take based on their current situation. In this paper, researchers created a new way for players to learn from experience and make good decisions. They came up with an idea called IGMM, which helps teams work together while also being selfish. Then, they built a special kind of artificial intelligence that can play the game well and make smart choices. This AI is better than other methods at solving this type of problem. |
Keywords
» Artificial intelligence » Online learning » Reinforcement learning
