Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
Authors: Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Boehmer, Shimon Whiteson
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,055 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,263 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on a set of exploration games, challenging cooperative predator-prey tasks requiring significant coordination among agents, and Star Craft II micromanagement benchmarks show that Une VEn can solve tasks where other state-of-the-art MARL methods fail. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford, Oxford, United Kingdom 2Department of Software Technology, Delft University of Technology, Delft, Netherlands. |
| Pseudocode | No | The main text of the paper refers to "Appendix B" for a detailed algorithm, but Appendix B itself is not included in the provided text. |
| Open Source Code | No | The paper mentions videos of learnt policies available at a URL (https://sites.google.com/view/uneven-marl/) but does not provide a statement or link for the open-source code of their methodology. |
| Open Datasets | Yes | We now evaluate Une VEn on challenging cooperative Star Craft II (SC2) maps from the popular SMAC benchmark (Samvelyan et al., 2019). |
| Dataset Splits | No | The paper describes training duration in steps and testing with rollouts (e.g., 'training for 35k steps', 'test 60 rollouts') within simulation environments, but does not provide traditional train/validation/test dataset splits. |
| Hardware Specification | No | The paper acknowledges 'a generous equipment grant from NVIDIA' but does not specify any particular GPU models, CPU models, or other hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and benchmarks (e.g., VDN, QMIX, Star Craft II), but it does not specify software names with their version numbers required for replication. |
| Experiment Setup | Yes | α is annealed from 0.3 to 1.0 in our experiments over a fixed number of steps at the beginning of training. Once this exploration stage is finished (i.e., α = 1), actions are always taken based on the target task s joint action value function. |