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..
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 | Venue PDF | 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. |