Notice: 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. Full accuracy metrics and methodology are described in [1].
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
Authors: Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As this is a theoretical contribution, we do not envision that our direct results will have a tangible societal impact. |
| Researcher Affiliation | Collaboration | Chi Jin Princeton University EMAIL Sham M. Kakade University of Washington Microsoft Research, NYC EMAIL Akshay Krishnamurthy Microsoft Research, NYC EMAIL Qinghua Liu Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1 Observable Operator Model with Upper Con๏ฌdence Bound (OOM-UCB) |
| Open Source Code | No | The paper is a theoretical contribution and does not mention releasing open-source code for the described methodology. |
| Open Datasets | No | The paper is a theoretical work focusing on algorithm design and theoretical guarantees (sample complexity), not on empirical evaluation using datasets. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is a theoretical work and does not describe empirical experiments. Therefore, no information about dataset splits for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is a theoretical work and does not describe empirical experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is a theoretical work and does not describe specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is a theoretical work and does not describe empirical experiments or their setup details such as hyperparameters or training configurations. |