Summary of Minimax-optimal Multi-agent Robust Reinforcement Learning, by Yuchen Jiao et al.
Minimax-Optimal Multi-Agent Robust Reinforcement Learning
by Yuchen Jiao, Gen Li
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper proposes a new algorithm for multi-agent robust reinforcement learning, which is essential for modeling competitive interactions under environmental uncertainties. The authors aim to overcome existing limitations in sample complexity by extending the Q-FTRL algorithm to finite-horizon settings and assuming access to a generative model. The proposed algorithm achieves an ε-robust coarse correlated equilibrium (CCE) with a sample complexity of O(H^3S∑i=1mA_i min{H,1/R}/ε^2), where H is the horizon length, S is the number of states, A_i is the number of actions for each agent, and R is the uncertainty level. The authors also show that this sample complexity is minimax optimal by combining an information-theoretic lower bound. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new method for teaching robots to work together in uncertain situations. By extending a previous algorithm called Q-FTRL, they can make robots achieve a good balance between cooperation and competition, even when the environment is changing or unpredictable. This is important because it allows multiple robots to work together effectively in real-world scenarios. |
Keywords
» Artificial intelligence » Generative model » Reinforcement learning
