Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
Authors: Dongsu Lee, Minhae Kwon
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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] |