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..
Opponent Modeling based on Subgoal Inference
Authors: XiaoPeng Yu, Jiechuan Jiang, Zongqing Lu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that our method achieves more effective adaptation than existing methods in a variety of tasks. |
| Researcher Affiliation | Academia | Xiaopeng Yu Jiechuan Jiang Zongqing Lu School of Computer Science, Peking University |
| Pseudocode | Yes | For completeness, the full procedure of OMG is given in Algorithm 1. |
| Open Source Code | Yes | To ensure reproducibility, we include the code in the supplementary material and will make it open-source upon acceptance. |
| Open Datasets | Yes | Foraging [2, 4] is an 8 8 gridworld... Predator-Prey [20] is a three-against-one multi-agent environment... SMAC [35] is a high-dimensional environment for collaborative multi-agent reinforcement learning based on Star Craft II |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' but does not explicitly specify a separate 'validation' set or its split percentages/counts for model selection during training. |
| Hardware Specification | Yes | The computational resources for the experiments are as follows: the CPU is Intel(R) Xeon(R) Platinum 8280 CPU @ 2.70GHz, and the GPU is A100-PCIE-40GB. |
| Software Dependencies | No | Table 1 lists various components like 'MLP', 'RNN', 'Re LU', 'Adam', 'RMSProp', 'CVAE' but does not provide specific version numbers for software libraries or dependencies (e.g., PyTorch 1.x, Python 3.x). |
| Experiment Setup | Yes | All hyperparameters are listed in Table 1. |