Summary of Choice Between Partial Trajectories: Disentangling Goals From Beliefs, by Henrik Marklund and Benjamin Van Roy
Choice Between Partial Trajectories: Disentangling Goals from Beliefs
by Henrik Marklund, Benjamin Van Roy
First submitted to arxiv on: 30 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 Machine learning researchers have developed sophisticated AI agents that mimic human behaviors. However, manually guiding these agents with human preferences becomes increasingly difficult as they become more advanced. To overcome this challenge, some propose training AI agents using human choice data, which requires a model of choice behavior that can interpret the data. For example, when AI agents make decisions between partial trajectories of states and actions, previous models assume the probability of choosing one option over another is determined by the partial return or cumulative advantage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI has become very smart, but it’s hard to tell them what to do. Instead of telling them what to do, some people think we should teach AI agents to learn from human choices. This means we need a way for the agent to understand what humans are choosing. For example, when an AI agent decides between two options, previous models assume that choice is based on how good or bad each option is. |
Keywords
» Artificial intelligence » Machine learning » Probability
