Replicability in Reinforcement Learning
Authors: Amin Karbasi, Grigoris Velegkas, Lin Yang, Felix Zhou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We initiate the mathematical study of replicability as an algorithmic property in the context of reinforcement learning (RL). Our work focuses solely on the setting with the generative model. We derive upper bounds on the sample complexity in both settings and validate their results experimentally. On the other hand, our work focuses solely on the setting with the generative model. |
| Researcher Affiliation | Collaboration | Amin Karbasi Yale University, Google Research amin.karbasi@yale.edu Grigoris Velegkas Yale University grigoris.velegkas@yale.edu Lin F. Yang UCLA linyang@ee.ucla.edu Felix Zhou Yale University felix.zhou@yale.edu |
| Pseudocode | Yes | Algorithm A.1 TV Indistinguishable Oracle for Multiple Query Estimation, Algorithm A.2 Sampling from Pairwise Optimal Coupling; [Angel and Spinka, 2019] |
| Open Source Code | No | The paper does not provide any statements about releasing source code for the methodology described, nor does it provide links to any code repositories for its own work. It mentions open-source code in the context of related work by other researchers. |
| Open Datasets | No | The paper is theoretical and does not use or reference any specific datasets for training or evaluation. It assumes access to a 'generative model' which is a theoretical construct for sampling, not a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits. Therefore, it does not provide information on training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical properties and algorithms; it does not describe any experimental setup or mention specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not provide details on specific software dependencies with version numbers required for implementation or reproduction. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical bounds, not practical experimental setups. Therefore, it does not provide details on hyperparameters or training settings. |