Searching for Programmatic Policies in Semantic Spaces
Authors: Rubens O. Moraes, Levi H. S. Lelis
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our hypothesis in a real-time strategy game called Micro RTS. Empirical results support our hypothesis that searching in semantic spaces can be more sample-efficient than searching in syntaxbased spaces. |
| Researcher Affiliation | Academia | Rubens O. Moraes1 and Levi H. S. Lelis2,3 1 Departamento de Inform atica, Universidade Federal de Vic osa, Brazil 2Department of Computing Science, University of Alberta, Canada 3Alberta Machine Intelligence Institute (Amii) rubens.moraes@ufv.br, levi.lelis@ualberta.ca |
| Pseudocode | Yes | Algorithm 1 Library Construction |
| Open Source Code | Yes | The implementation of our system is available online at https: //github.com/rubensolv/Library-Induced-Semantic-Spaces |
| Open Datasets | Yes | We evaluate LISS using the Micro RTS domain, a real-time strategy game designed for research. There is an active research community that uses Micro RTS as a benchmark to evaluate intelligent systems.2 (Footnote 2: https://github.com/Farama-Foundation/Micro RTS/wiki) |
| Dataset Splits | No | No specific numerical splits (e.g., percentages, exact counts) for training, validation, and testing datasets were provided. The paper refers to different MDPs (Ptrain, Ptest) rather than data splits of a single dataset. |
| Hardware Specification | Yes | We used a dedicated number of computers with the following settings: 16 GB of RAM, i7-1165G7 CPUs at 2.80 GHz with 8 threads. |
| Software Dependencies | No | The paper mentions 'Microlanguage' and 'Python' but does not specify version numbers for programming languages, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | We use k = 1000 in Nk and a limit of 400 seconds for SHC to return a best response; once it reaches this time limit, it returns the best policy it encountered across all restarts of the search. We use ϵ = 0.20 for mixing the syntax and semantic spaces. We also use z = 4 in all our experiments. |