Summary of Innate-values-driven Reinforcement Learning For Cooperative Multi-agent Systems, by Qin Yang
Innate-Values-driven Reinforcement Learning for Cooperative Multi-Agent Systems
by Qin Yang
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Robotics (cs.RO)
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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 Innate-Values-Driven Reinforcement Learning (IVRL) model tackles a crucial problem in multi-agent systems: balancing utilities and system costs to satisfy group members’ needs. Building on reinforcement learning’s reward-driven behaviors, IVRL describes the complex interactions of AI agents in cooperation. A hierarchical architecture is implemented in the StarCraft Multi-Agent Challenge (SMAC) environment, comparing cooperative performance across three innate value agent types (Coward, Neutral, and Reckless) using QMIX, IQL, and QTRAN algorithms. The results show that rational organization of individual needs leads to better performance with lower costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI agents are driven by their own motivations and interests, which is called innate values. This paper wants to help AI agents work together in teams more effectively. They propose a new way to train these AI agents using a type of learning called reinforcement learning. This method helps the agents learn from their actions and rewards. The authors tested this approach on a game-like environment where multiple AI agents need to work together. They found that by considering each agent’s different needs, the group can achieve better results with fewer costs. |
Keywords
* Artificial intelligence * Reinforcement learning
