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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

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.