Unveiling Options with Neural Network Decomposition
Authors: Mahdi Alikhasi, Levi Lelis
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results in two grid-world domains where exploration can be difficult confirm that our method can identify useful options, thereby accelerating the learning process on similar but different tasks. |
| Researcher Affiliation | Academia | Mahdi Alikhasi and Levi H. S. Lelis Amii, Department of Computing Science, University of Alberta {alikhasi,levi.lelis}@ualberta.ca |
| Pseudocode | Yes | Algorithm 1 COMPUTE-LOSS |
| Open Source Code | Yes | The implementation used in our experiments is available online.1 1https://github.com/lelis-research/Dec-Options |
| Open Datasets | Yes | Mini Grid (Chevalier-Boisvert et al., 2023) |
| Dataset Splits | No | The paper mentions using training and validation sets but does not provide specific details on the split percentages or how the data is partitioned for these sets. |
| Hardware Specification | No | The paper mentions support from 'Digital Research Alliance of Canada' but does not provide specific hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions using 'Stable-baselines framework (Raffin et al., 2021)' but does not provide specific version numbers for this or other software dependencies. |
| Experiment Setup | Yes | Additional details, including agent architectures, hyperparameter settings, and used libraries are provided in the Appendix. |