Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Exploration in Structured Reinforcement Learning
Authors: Jungseul Ok, Alexandre Proutiere, Damianos Tranos
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,025 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,547 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary numerical experiments (presented in the supplementary material) illustrate our theoretical ๏ฌndings. |
| Researcher Affiliation | Academia | Jungseul Ok KTH, EECS Stockholm, Sweden EMAIL Alexandre Proutiere KTH, EECS Stockholm, Sweden EMAIL Damianos Tranos KTH, EECS Stockholm, Sweden EMAIL |
| Pseudocode | Yes | Algorithm 1 DEL(ฮณ) |
| Open Source Code | No | The paper mentions 'Preliminary numerical experiments (presented in the supplementary material)' but does not provide an explicit statement or link for open-source code. |
| Open Datasets | No | The paper mentions 'Preliminary numerical experiments (presented in the supplementary material)' but does not provide any concrete access information (link, DOI, specific citation with authors/year, or repository) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions 'Preliminary numerical experiments' but does not provide specific dataset split information (percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper mentions 'Preliminary numerical experiments' but does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Preliminary numerical experiments' but does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper mentions 'Preliminary numerical experiments' but does not provide specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |