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..
Playing Card-Based RTS Games with Deep Reinforcement Learning
Authors: Tianyu Liu, Zijie Zheng, Hongchang Li, Kaigui Bian, Lingyang Song
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are performed on Clash Royale1, a popular mobile card-based RTS game. Empirical results show that the SEAT model agent makes it to reach a high winning rate against rule-based agents and decision-treebased agent. |
| Researcher Affiliation | Collaboration | Tianyu Liu1 , Zijie Zheng1 , Hongchang Li2 , Kaigui Bian1 and Lingyang Song1 1School of EECS, Peking University 2Babeltime Technology Co. |
| Pseudocode | Yes | Algorithm 1 SEAT Model |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | A simulation environment of CR on Windows platform is produced for experiments of our model. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions that "The SEAT model is implemented in Py Torch" but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Hyperparameters not specified in last section include discount factor γ (set as 1), the update period N of θ = θ (set as 10) and the abandoning-rate ϵ (set as 0.0 and 0.5 for contrast experiments). |