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..
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
Authors: Dongsu Lee, Minhae Kwon
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the proposed mechanism, we consider the multi-agent autonomous driving scenario with diverse driving traits and multiple particle environments. Simulation results demonstrate that the EFT mechanism with accurate character inference leads to a higher reward than existing multi-agent solutions. |
| Researcher Affiliation | Academia | Dongsu Lee and Minhae Kwon Department of Intelligent Semiconductors School of Electronic Engineering Soongsil University, Seoul, South Korea |
| Pseudocode | Yes | Algorithm 1 Multi-character policy training; Algorithm 2 Character inference module; Algorithm 3 Episodic future thinking mechanism |
| Open Source Code | No | Project Web: https://sites.google.com/view/eftm-neurips2024. |
| Open Datasets | Yes | To implement this task, we use the FLOW framework [58, 21, 8]. The scenario includes multiple automated vehicles on the highway. ... To confirm the scalability of the proposed solution, we also provide simulation results with a multiple particle environment (MPE) [29] and starcraft multi-agent challenge (SMAC) [48], a popular MARL testbed. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | Yes | Appendix B System Specification CPU AMD Ryzen 9 3950X 16-core GPU Ge Forece RTX 2080 Ti RAM 128 GB SSD 1T |
| Software Dependencies | No | Appendix C.1 and C.2 list hyperparameters for Algorithm 1 and Algorithm 2 but do not specify software dependencies (e.g., programming languages, libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | Appendix C Hyperparameters C.1 Algorithm 1 [list of hyperparameters and values]; C.2 Algorithm 2 [list of hyperparameters and values] |