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..
Off-Agent Trust Region Policy Optimization
Authors: Ruiqing Chen, Xiaoyuan Zhang, Yali Du, Yifan Zhong, Zheng Tian, Fanglei Sun, Yaodong Yang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on the Star Craft II Multi-Agent Challenge (SMAC) and Google Research Football (GRF) demonstrate that our algorithms outperform state-of-the-art (SOTA) methods and achieve faster convergence, suggesting the viability of our approach for efficient experience reusing in MARL. |
| Researcher Affiliation | Academia | Ruiqing Chen1,2, , Xiaoyuan Zhang1, , Yali Du3, Yifan Zhong1, Zheng Tian2, Fanglei Sun2 and Yaodong Yang1, 1Institute for AI, Peking University, Beijing, China 2Shanghai Tech University, Shanghai, China 3King s College London, UK EMAIL |
| Pseudocode | Yes | Algorithm 1 Off-agent Policy Iteration with Approximate Monotonic Improvement |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | Yes | Experiments conducted on the Star Craft II Multi-Agent Challenge (SMAC) and Google Research Football (GRF) demonstrate that our algorithms outperform state-of-the-art (SOTA) methods and achieve faster convergence... SMAC [Samvelyan et al., 2019]... GRF [Kurach et al., 2020] |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits within the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper states 'Experiment details are in Appendix I/J/K' but does not include specific experimental setup details (like hyperparameter values or training configurations) in the main text. |