Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation
Authors: Ziyang Tang*, Yihao Feng*, Lihong Li, Dengyong Zhou, Qiang Liu
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both theoretical and empirical results show that our method yields significant advantages over previous methods. |
| Researcher Affiliation | Collaboration | Ziyang Tang * The University of Texas at Austin ztang@cs.utexas.edu Yihao Feng The University of Texas at Austin yihao@cs.utexas.edu Lihong Li Google Research lihong@google.com Dengyong Zhou Google Research dennyzhou@google.com Qiang Liu The University of Texas at Austin lqiang@cs.utexas.edu |
| Pseudocode | Yes | Algorithm 1 Infinite Horizon Doubly Robust Estimator |
| Open Source Code | No | The paper mentions using 'open source implementation' (footnote 2) for deep Q-learning, which points to a third-party repository. It also provides a link for 'additional experimental results' (footnote 3) but does not contain an unambiguous statement of releasing the specific code for their *own* methodology. |
| Open Datasets | Yes | Taxi Environment We follow Liu et al. (2018a) s tabular environment Taxi |
| Dataset Splits | No | The paper mentions using 'a set of independent sample to first train a value function b V and a density function bρ' and 'a seperate training dataset with 200 trajectories whose horizon length is 1000', but does not provide specific numerical splits (e.g., percentages or counts) for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Open AI Gym' and 'Adam Optimizer' but does not provide specific version numbers for any software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | For more experimental details, please check appendix C.1. |