Summary of Select to Perfect: Imitating Desired Behavior From Large Multi-agent Data, by Tim Franzmeyer et al.
Select to Perfect: Imitating desired behavior from large multi-agent data
by Tim Franzmeyer, Edith Elkind, Philip Torr, Jakob Foerster, Joao Henriques
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed paper investigates the development of AI agents that learn from demonstrations of human behavior, with a focus on ensuring safe and desirable outcomes. To achieve this, the authors assign desirability scores to collective trajectories rather than individual behaviors, allowing them to assess the impact of each agent’s behavior on the overall score. The concept of an agent’s Exchange Value is introduced, which quantifies its contribution to the collective desirability score. By estimating Exchange Values from real-world datasets, the authors develop methods for learning desired imitation policies that outperform baselines. This work has significant implications for AI training and decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI agents are trained using big datasets of human behavior. But not all behaviors are safe or good. The paper shows how to make an AI agent learn from what humans do, but only if it’s safe and desirable. To do this, the authors give scores to groups of actions, not individual actions. They also create a special value called Exchange Value that tells us how much an agent’s behavior changes the overall score. By using real-world data, they can teach AI agents to copy good behaviors and avoid bad ones. |
