Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Authors: Yuchen Xiao, Weihao Tan, Christopher Amato
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results (in simulation and hardware) in a variety of realistic domains demonstrate the superiority of our approaches in large multi-agent problems and validate the effectiveness of our algorithms for learning high-quality and asynchronous solutions. |
| Researcher Affiliation | Academia | Yuchen Xiao Khoury College of Computer Sciences Northeastern University Boston, MA 02115 xiao.yuch@northeastern.edu Weihao Tan Khoury College of Computer Sciences Northeastern University Boston, MA 02115 w.tan@northeastern.edu Christopher Amato Khoury College of Computer Sciences Northeastern University Boston, MA 02115 c.amato@northeastern.edu |
| Pseudocode | Yes | The pseudocode and detailed trajectory squeezing process for each proposed method are presented in Appendix C. |
| Open Source Code | Yes | In supplementary materials, we include the code and a README.txt file to reproduce the main experimental results. |
| Open Datasets | Yes | We investigate the performance of our algorithms over a variety of multi-agent problems with macroactions (Fig. 1): Box Pushing [Xiao et al., 2019], Overcooked [Wu et al., 2021b], and a larger Warehouse Tool Delivery [Xiao et al., 2019] domain. |
| Dataset Splits | No | The paper refers to 'training trials' and 'testing episodes' for evaluation but does not specify explicit dataset splits (e.g., percentages or counts for training, validation, and test sets). |
| Hardware Specification | Yes | The details of used computational resources are mentioned in Appendix E. |
| Software Dependencies | No | The provided text does not explicitly list software dependencies with specific version numbers. |
| Experiment Setup | Yes | All the training details including hyperparameters are in Appendix E. |