Summary of Aligning Ai Agents Via Information-directed Sampling, by Hong Jun Jeon et al.
Aligning AI Agents via Information-Directed Sampling
by Hong Jun Jeon, Benjamin Van Roy
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores AI Alignment by extending classic multi-armed bandit problems to a new class of bandit alignment problems. The latter involve an agent maximizing long-run expected reward through interactions with an environment and human, with details/preferences initially unknown. The agent must balance exploration (of the environment and human) and exploitation while learning preferences from human feedback, considering associated costs. The paper studies these trade-offs theoretically and empirically in a toy bandit alignment problem, demonstrating that naive algorithms and even Thompson sampling fail to provide acceptable solutions. Instead, information-directed sampling achieves favorable regret. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers are working on aligning superintelligent AI agents with human interests. This paper looks at how an agent can learn from humans while trying to achieve a goal. The challenge is that the agent doesn’t know what the human likes or dislikes at first. The agent has to figure this out by interacting with both the environment and the human, while also avoiding wasting time asking for preferences. The researchers test different methods in a simple scenario and find that one approach called information-directed sampling does better than others. |
Keywords
» Artificial intelligence » Alignment
