Summary of Independent Rl For Cooperative-competitive Agents: a Mean-field Perspective, by Muhammad Aneeq Uz Zaman et al.
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective
by Muhammad Aneeq uz Zaman, Alec Koppel, Mathieu Laurière, Tamer Başar
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
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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 proposes a reinforcement learning method that achieves a Nash equilibrium in a setting where agents are grouped into teams and there is cooperation within each team but competition across different teams. The authors focus on a linear-quadratic structure and consider the mean-field setting, resulting in a General-Sum LQ Mean-Field Type Game (GS-MFTG). They characterize the Nash equilibrium of the GS-MFTG and show that it is approximately Nash-equivalent for the finite population game. An algorithm called Multi-player Receding-horizon Natural Policy Gradient (MRNPG) is proposed, which converges to a global Nash equilibrium through a novel decomposition using backward recursive discrete-time Hamilton-Jacobi-Isaacs equations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how groups of agents work together and compete with each other. It’s like a big game where teams are working together, but also trying to win against the other teams. The researchers came up with a special way for the agents to learn and make decisions that will help them achieve a good outcome. They tested this method on some fake data and it worked pretty well. |
Keywords
* Artificial intelligence * Reinforcement learning
