Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
Authors: Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,116 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,176 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform extensive experimental studies over many challenging battle games in Star Craft II based on the learning environment SC2LE (Vinyals et al., 2017). |
| Researcher Affiliation | Collaboration | 1Tencent AI Lab, Shenzhen, China 2University of Technology Sydney, Australia 3UBTECH SAIC, Univ. of Sydney, Australia 4Northwestern University, IL, USA 5Hong Kong University of Science and Technology, Hong Kong, China. |
| Pseudocode | No | The paper describes algorithms and architectures but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide a specific link to the source code for the methodology described, nor does it explicitly state that the code is open-source or available in supplementary materials. |
| Open Datasets | Yes | In this section, we perform extensive experimental studies over many challenging battle games in Star Craft II based on the learning environment SC2LE (Vinyals et al., 2017). |
| Dataset Splits | No | The paper describes training and testing phases with different randomization, but does not provide specific dataset split percentages, sample counts, or explicit mention of a validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using PPO and an architecture similar to IMPALA but does not provide specific version numbers for any software dependencies required for replication. |
| Experiment Setup | No | The detailed network structures and the training settings are provided in the supplementary material. |