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.
Adversarial Policy Learning in Two-player Competitive Games
Authors: Wenbo Guo, Xian Wu, Sui Huang, Xinyu Xing
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,219 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,176 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 evaluate our proposed learning algorithm by using five selected games (i.e., four Mu Jo Co games and Star Craft II). |
| Researcher Affiliation | Collaboration | 1College of Information Sciences and Technology, The Pennsylvania State University, State College, PA, USA 2Netflix Inc., Los Gatos, CA, USA. |
| Pseudocode | No | The paper describes the learning algorithm in text and mathematical equations but does not include a structured pseudocode or algorithm block in the main paper. |
| Open Source Code | Yes | We released our source code to support future research.1 https://github.com/psuwuxian/rl_adv_ valuediff |
| Open Datasets | Yes | In this section, we evaluate our proposed learning algorithm by using five selected games (i.e., four Mu Jo Co games and Star Craft II). (Todorov et al., 2012) (Sun et al., 2018) |
| Dataset Splits | No | The paper describes training adversarial agents in game environments but does not provide specific train/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Due to space limit, we specify the implementation details and experiment setup (i.e., game and victim policy selection, evaluation metric, hyperparameters) in Supplementary Section S5. |