Summary of Getting by Goal Misgeneralization with a Little Help From a Mentor, By Tu Trinh et al.
Getting By Goal Misgeneralization With a Little Help From a Mentor
by Tu Trinh, Mohamad H. Danesh, Nguyen X. Khanh, Benjamin Plaut
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates how reinforcement learning (RL) agents can better handle distribution shifts in real-world deployments. Specifically, it explores whether allowing an RL agent to request help from a supervisor in unfamiliar situations can mitigate goal misgeneralization, where the agent learns a proxy goal during training but not during deployment. The study focuses on PPO-trained agents in the CoinRun environment, known for exhibiting goal misgeneralization. The authors evaluate various methods for determining when the agent should ask for help and find that consistently requesting help improves performance. However, they also discover that methods based on internal state fail to proactively request help, waiting instead until mistakes occur. This highlights the importance of learning nuanced representations, avoiding ignoring irrelevant information, and developing ask-for-help strategies tailored to the training algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) agents can be helped when they don’t know what to do. When AI agents are trained, they sometimes learn things that help them get rewards, but these same tricks might not work in real life. This can cause problems because the agent may not understand what it’s supposed to do anymore. The researchers tested an idea where the agent asks for help from someone smarter when it doesn’t know what to do. They found that this helps a lot! But they also discovered that some methods aren’t very good at figuring out when to ask for help. This is important because AI agents need to learn how to make good decisions on their own, and sometimes asking for help is the best thing to do. |
Keywords
* Artificial intelligence * Reinforcement learning
