Summary of Reclaiming the Source Of Programmatic Policies: Programmatic Versus Latent Spaces, by Tales H. Carvalho et al.
Reclaiming the Source of Programmatic Policies: Programmatic versus Latent Spaces
by Tales H. Carvalho, Kenneth Tjhia, Levi H. S. Lelis
First submitted to arxiv on: 16 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 paper introduces LEAPS and HPRL systems that learn latent spaces of domain-specific languages, used to define programmatic policies for POMDPs. These systems optimize losses like behavior loss to achieve locality in program behavior. The authors show that the programmatic space induced by a domain-specific language presents similar values for the behavior loss as previous work’s latent spaces. They also find that algorithms searching in this space outperform those in LEAPS and HPRL. To explain their results, they measured the “friendliness” of these spaces to local search algorithms. They found that algorithms are more likely to stop at local maxima when searching in the latent space than in the programmatic space. This implies that the programmatic space’s optimization topology is more conducive to search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers learn from languages and uses this knowledge to make better decisions. It shows that a special kind of computer program can learn from a language and use it to make good choices. The authors found that this program is actually better at making choices than other programs that do the same thing. They think this might be because the program’s “neighborhood” (how it looks at things around it) helps it find better solutions. |
Keywords
» Artificial intelligence » Latent space » Optimization
