Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning
Authors: Hyungho Na, Yunkyeong Seo, Il-chul Moon
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is evaluated in Star Craft II and Google Research Football, and empirical results indicate further performance improvement over state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Hyungho Na1, Yunkyeong Seo1 & Il-Chul Moon1,2 1Korea Advanced Institute of Science and Technology (KAIST), 2summary.ai |
| Pseudocode | Yes | Algorithm 1 Training Algorithm for State Embedding Algorithm 3 EMU: Efficient episodic Memory Utilization for MARL |
| Open Source Code | Yes | Our code is available at: https://github.com/Hyungho Na/EMU. |
| Open Datasets | Yes | We conduct experiments on complex multi-agent tasks such as SMAC (Samvelyan et al., 2019) and GRF (Kurach et al., 2020). |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly describe a separate 'validation' dataset split with specific percentages or counts. |
| Hardware Specification | Yes | Experiments for SMAC (Samvelyan et al., 2019) are mainly carried out on NVIDIA Ge Force RTX 3090 GPU |
| Software Dependencies | No | We utilize Py MARL (Samvelyan et al., 2019) to execute all of the baseline algorithms with their open-source codes... All SMAC experiments were conducted on Star Craft II version 4.10.0 in a Linux environment. The paper mentions these software components but does not provide version numbers for all key dependencies like Python, PyTorch, etc. |
| Experiment Setup | Yes | Table 1: Task-dependent hyperparameter of EMU. Table 8: EMU Hyperparameters for fϕ and fψ training. |