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..
Learning Nearly Decomposable Value Functions Via Communication Minimization
Authors: Tonghan Wang*, Jianhao Wang*, Chongyi Zheng, Chongjie Zhang
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate that, on the Star Craft unit micromanagement benchmark, our framework significantly outperforms baseline methods and allows us to cut off more than 80% of communication without sacrificing the performance. |
| Researcher Affiliation | Academia | 1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China 2Turing AI Institute of Nanjing, Nanjing, China |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper only provides a link to videos of experiments (https://sites.google.com/view/ndq) and does not state that the source code for the methodology is openly available or provide a link to a code repository. |
| Open Datasets | Yes | We demonstrate the effectiveness of our learning framework on Star Craft II1 unit micromanagement benchmark used in Foerster et al. (2017; 2018); Rashid et al. (2018); Samvelyan et al. (2019). |
| Dataset Splits | No | The paper mentions training and testing, but does not explicitly describe a validation dataset split (e.g., percentages or counts for a validation set). |
| Hardware Specification | Yes | We train our models on NVIDIA RTX 2080Ti GPUs using experience sampled from 16 parallel environments. |
| Software Dependencies | No | The paper mentions basing the implementation on the PyMARL framework but does not provide specific version numbers for software dependencies such as Python, PyTorch, or PyMARL itself. |
| Experiment Setup | Yes | We use the same hyper-parameter setting for NDQ on all maps: β is set to 10 5, λ is set to 0.1, and the length of message mij is set to 3. |