Summary of Multi-agent Reinforcement Learning with Deep Networks For Diverse Q-vectors, by Zhenglong Luo et al.
Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors
by Zhenglong Luo, Zhiyong Chen, James Welsh
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper proposes a novel deep Q-networks (DQN) algorithm for multi-agent reinforcement learning (MARL), which enables agents to learn various Q-vectors using Max, Nash, and Maximin strategies. The proposed algorithm is evaluated in an environment where two robotic arms collaborate to lift a pot, demonstrating its effectiveness in achieving optimal policies. The paper builds upon existing research in MARL, including the study of Nash equilibria and algorithms like Nash Q-learning and Nash Actor-Critic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to help robots work together better. Imagine you have two robotic arms that need to lift a heavy pot together. To figure out how to do this, the algorithm learns from its mistakes and adjusts its actions accordingly. The researchers tested their approach in a scenario where the robots need to lift the pot, and it worked well. |
Keywords
» Artificial intelligence » Reinforcement learning
