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..
Toward Efficient Multi-Agent Exploration With Trajectory Entropy Maximization
Authors: Tianxu Li, Kun Zhu
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the effectiveness of our method, we test our method in challenging multi-agent tasks from several MARL benchmarks. The results demonstrate that our method consistently outperforms existing state-of-the-art methods. In this section, we examine the performance of our proposed TEE method using challenging multi-agent tasks from Pac-Men, SMAC, and SMACv2 benchmarks, demonstrating its superior effectiveness. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China 2Collaborative Innovation Center of Novel Software Technology and Industrialization EMAIL |
| Pseudocode | Yes | For the Py Torch-style pseudocode of TEE, please refer to Appendix E. The source code of our method can be found in the supplemental material. |
| Open Source Code | Yes | For the Py Torch-style pseudocode of TEE, please refer to Appendix E. The source code of our method can be found in the supplemental material. |
| Open Datasets | Yes | In this section, we examine the performance of our proposed TEE method using challenging multi-agent tasks from Pac-Men, SMAC, and SMACv2 benchmarks... The StarCraft Multi-Agent Challenge (SMAC) (Samvelyan et al., 2019)... SMACv2 benchmark (Ellis et al., 2022). |
| Dataset Splits | No | The paper describes the experimental environments and training/testing procedures (e.g., 'We set the evaluation interval to 10K steps followed by 32 test episodes. We run all methods for 5 million steps.'), but does not provide traditional training/validation/test dataset splits as commonly found in supervised learning tasks since it uses interactive reinforcement learning environments. |
| Hardware Specification | Yes | We implemented our method using Num Py and Py Torch, and all experiments are conducted on a single NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | Yes | The SC2.4.10 version of Star Craft II is used, and it s important to note that performance comparisons between different versions are not applicable. |
| Experiment Setup | Yes | The hyperparameters for TEE and baseline methods in Pac-Men, SMAC, and SMACv2 are detailed in Table 2. |