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..
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning
Authors: Hyungho Na, Yunkyeong Seo, Il-chul Moon
ICLR 2024 | Venue PDF | 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. |