Toward Efficient Multi-Agent Exploration With Trajectory Entropy Maximization
Authors: Tianxu Li, Kun Zhu
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | 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. |