FoX: Formation-Aware Exploration in Multi-Agent Reinforcement Learning
Authors: Yonghyeon Jo, Sunwoo Lee, Junghyuk Yeom, Seungyul Han
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results show that the proposed Fo X framework significantly outperforms the state-of-the-art MARL algorithms on Google Research Football (GRF) and sparse Starcraft II multi-agent challenge (SMAC) tasks. |
| Researcher Affiliation | Academia | Artificial Intelligence Graduate School, UNIST, Ulsan, South Korea |
| Pseudocode | Yes | We summarize the proposed Fo X framework as Algorithm 1 and Figure 5. |
| Open Source Code | Yes | The source code of our proposed algorithm is available at https://github.com/hyeon1996/Fo X. |
| Open Datasets | Yes | In our experiments, we evaluate the performance of the proposed Fo X framework in the challenging cooperative multi-agent environments: the sparse Star Craft multi-agent challenge (SMAC) (Samvelyan et al. 2019) and Google Research Football (GRF) (Kurach et al. 2020). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions various MARL algorithms and environments (e.g., QMIX, SMAC, GRF) used in the experiments, but does not provide specific ancillary software details like library or solver names with version numbers. |
| Experiment Setup | Yes | For hyperparameters β1, β2, we have tested β1 {0.001, 0.005, 0.01, 0.02, 0.1} and β2 {0.001, 0.005, 0.01, 0.05} for both environments. |