GoBigger: A Scalable Platform for Cooperative-Competitive Multi-Agent Interactive Simulation
Authors: Ming Zhang, Shenghan Zhang, Zhenjie Yang, Lekai Chen, Jinliang Zheng, Chao Yang, Chuming Li, Hang Zhou, Yazhe Niu, Yu Liu
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate several state-of-the-art algorithms on Go Bigger and demonstrate the potential of the environment. |
| Researcher Affiliation | Collaboration | Sense Time Research, Shanghai AI Laboratory, Shanghai, China |
| Pseudocode | No | The paper describes algorithms for collision detection and neural network architectures, but these are presented as textual descriptions and figures, not formal pseudocode blocks or algorithms. |
| Open Source Code | Yes | Both Go Bigger and its related benchmark are open-sourced. More information could be found at https://github.com/opendilab/Go Bigger. |
| Open Datasets | No | The paper utilizes its own simulation environment, Go Bigger, for experiments rather than a pre-existing, publicly available dataset in the typical sense. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits (e.g., percentages or counts) for reproduction. It evaluates agents within a continuous simulation environment. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models) used to run the experiments. |
| Software Dependencies | No | The paper mentions using established reinforcement learning algorithms and environment design principles (e.g., gym), but it does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | Appendix B.3 provides detailed experimental setups for each algorithm, including hyperparameters: "DQN The batch_size is set as 512. learning_rate is 1e-4, replay_buffer_size is 2e4." and similar for PPO, QMIX, MAPPO, VMIX, COMA. |