Summary of The Benefits Of Power Regularization in Cooperative Reinforcement Learning, by Michelle Li and Michael Dennis
The Benefits of Power Regularization in Cooperative Reinforcement Learning
by Michelle Li, Michael Dennis
First submitted to arxiv on: 17 Jun 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 A novel approach to Cooperative Multi-Agent Reinforcement Learning (MARL) aims to mitigate the concentration of power by explicitly regularizing it. This concentration can lead to a single point of failure, where the failure or adversarial intent of one agent can harm the entire system. The authors propose a practical measure of power and a power-regularized objective that balances task reward with power distribution. Two algorithms are developed: Sample Based Power Regularization (SBPR) injects adversarial data during training, while Power Regularization via Intrinsic Motivation (PRIM) adds an intrinsic motivation to regulate power. Experimental results show that both algorithms successfully balance task reward and power, preventing catastrophic events when an agent goes off-policy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research focuses on making teams of people or AI agents work together better by sharing power fairly. When one person or agent has too much control, it can be a problem if they fail or have bad intentions. To fix this, the researchers created a new way to measure and manage power distribution in groups. They developed two methods: one that adds fake data during training and another that encourages agents to cooperate by sharing rewards. The results show that these methods help teams work together more effectively. |
Keywords
» Artificial intelligence » Regularization » Reinforcement learning
