Summary of Searching For Programmatic Policies in Semantic Spaces, by Rubens O. Moraes and Levi H. S. Lelis
Searching for Programmatic Policies in Semantic Spaces
by Rubens O. Moraes, Levi H. S. Lelis
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
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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 alternative method for synthesizing programmatic policies searches within an approximation of the language’s semantic space, which is hypothesized to be more sample-efficient compared to syntax-based spaces. The search algorithm evaluates different agent behaviors as it searches through the space, unlike syntax-based spaces where small changes in syntax may not result in different behaviors. The semantic space is defined by learning a library of programs that present different agent behaviors, and a neighborhood function is used for local search algorithms to replace parts of the current candidate program with programs from the library. The proposed method was evaluated in MicroRTS, a real-time strategy game, and empirical results support the hypothesis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to create computer programs that make decisions based on policies. Instead of searching through a set of possible programs like we usually do, this approach looks at the meaning or “semantics” behind the programs. It’s like trying to find a policy by understanding what different actions mean in a game rather than just looking at the specific rules. The researchers tested their idea using a video game called MicroRTS and found that it can be more efficient than traditional methods. |
Keywords
» Artificial intelligence » Semantics » Syntax
