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].
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. |