Value-Decomposition Multi-Agent Actor-Critics

Authors: Jianyu Su, Stephen Adams, Peter Beling11352-11360

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In this section, we benchmark VDACs against the baseline algorithms listed in Table 1 on a standardized decentralised Star Craft II micromanagement environment, SMAC (Samvelyan et al. 2019). SMAC consists of a set of Star Craft II micromanagement games that aim to evaluate how well independent agents are able to cooperate to solve complex tasks. In each scenario, algorithm-controlled ally units fight against enemy units controlled by the built-in game AI.
Researcher Affiliation Academia Jianyu Su, Stephen Adams, Peter Beling University of Virginia 151 Engineer s Way Charlottesville, Virginia, 22904 {js9wv, sca2c, pb3a}@virginia.edu
Pseudocode Yes The pseudo code is listed in Appendix.
Open Source Code No The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available.
Open Datasets Yes We benchmark VDACs against the baseline algorithms listed in Table 1 on a standardized decentralised Star Craft II micromanagement environment, SMAC (Samvelyan et al. 2019).
Dataset Splits No The paper does not provide specific percentages or counts for training, validation, and test dataset splits. It mentions 'evaluation episodes' but without detailed split methodology.
Hardware Specification No The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments.
Software Dependencies No The paper mentions software frameworks and environments like 'A2C framework' and 'SMAC', but does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or solvers).
Experiment Setup Yes Refer to Appendix for training details and map configuration.