Exploration in Structured Reinforcement Learning
Authors: Jungseul Ok, Alexandre Proutiere, Damianos Tranos
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary numerical experiments (presented in the supplementary material) illustrate our theoretical findings. |
| Researcher Affiliation | Academia | Jungseul Ok KTH, EECS Stockholm, Sweden ockjs@illinois.edu Alexandre Proutiere KTH, EECS Stockholm, Sweden alepro@kth.se Damianos Tranos KTH, EECS Stockholm, Sweden tranos@kth.se |
| Pseudocode | Yes | Algorithm 1 DEL(γ) |
| 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. |