Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
Authors: Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we report preliminary empirical results confirming our theoretical findings. [...] We observe that Dis Co outperforms Ucb Explore for any value of . |
| Researcher Affiliation | Collaboration | Jean Tarbouriech Facebook AI Research Paris & Inria Lille Matteo Pirotta Facebook AI Research Paris Michal Valko Deep Mind Paris Alessandro Lazaric Facebook AI Research Paris |
| Pseudocode | Yes | Algorithm 1: Algorithm Dis Co |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for their methodology is publicly available. |
| Open Datasets | No | The paper mentions environments like "confusing chain domain" and "combination lock problem introduced in [31]" for numerical simulations, but does not provide concrete access information (e.g., URL, DOI, repository, or formal citation for data) for these as publicly available datasets. |
| Dataset Splits | No | The paper mentions that "Values are averaged over 50 runs" but does not provide specific training, validation, or test dataset splits. The experimental setup uses simulated environments that generate data through interaction, not pre-defined static datasets with splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, processors, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. It only mentions general aspects like removing "logarithmic and constant terms for simplicity" and using "empirical Bernstein inequality". |
| Experiment Setup | No | The main text states: "We refer the reader to App. F for details on the algorithmic configurations and on the environments considered." This indicates that specific experimental setup details are not provided in the main body of the paper. |