Intrinsic Action Tendency Consistency for Cooperative Multi-Agent Reinforcement Learning
Authors: Junkai Zhang, Yifan Zhang, Xi Sheryl Zhang, Yifan Zang, Jian Cheng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on challenging tasks in SMAC and GRF benchmarks showcase the improved performance of our method. |
| Researcher Affiliation | Academia | Institute of Automation, Chinese Academy of Sciences 2School of Artificial Intelligence, University of Chinese Academy of Sciences 3University of Chinese Academy of Sciences, Nanjing 4Nanjing Artificial Intelligence Research of AI |
| Pseudocode | Yes | The pseudo-code of our algorithm is interpreted in the Appendix. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Extensive experiments on challenging tasks in SMAC and GRF benchmarks showcase the improved performance of our method. ... Star Craft II Micromanagement (Samvelyan et al. 2019) (SMAC) and Google Research Football (GRF) (Kurach et al. 2020) |
| Dataset Splits | No | The paper describes the experimental environments (SMAC, GRF) and the training paradigm, but does not explicitly provide details about training, validation, or test dataset splits in terms of percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or cloud computing instance types) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | No | The paper describes the general training paradigm (e.g., '32 parallel runners') but does not provide specific details about experimental setup such as hyperparameter values (e.g., learning rate, batch size, epochs). |