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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Causal Deep Reinforcement Learning Using Observational Data
Authors: Wenxuan Zhu, Chao Yu, Qiang Zhang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove the effectiveness of our deconfounding methods and validate them experimentally.The experimental results verify that the proposed deconfounding methods are effective: offline RL algorithms using deconfounding methods perform better on datasets with the confounders. |
| Researcher Affiliation | Academia | 1Dalian University of Technology 2Sun Yat-sen University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: "All the implementations of the offline RL algorithms in this paper follow d3rlpy, an offline RL library [Takuma Seno, 2021]." This refers to a third-party library used, not the authors' own code for their proposed methods. No explicit statement or link for the authors' code is provided. |
| Open Datasets | Yes | we design four benchmark tasks, namely, Emotional Pendulum, Windy Pendulum, Emotional Pendulum*, and Windy Pendulum*, by modifying the Pendulum task in the Open AI Gym [Brockman et al., 2016]. |
| Dataset Splits | No | The paper states: "The rewards are tested over 20 episodes every 1000 learning steps, and averaged over 5 random seeds." While it mentions testing, it does not provide specific training, validation, or test dataset split percentages or sample counts to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using "d3rlpy, an offline RL library [Takuma Seno, 2021]" but does not specify the version number of d3rlpy or any other key software components like Python, PyTorch/TensorFlow, or CUDA versions. |
| Experiment Setup | Yes | All the hyperparameters of the offline RL algorithms are set to the default values of d3rlpy. The rewards are tested over 20 episodes every 1000 learning steps, and averaged over 5 random seeds. Other hyperparameters and the implementation details are described in Appendix C. |