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..
Order Matters: Agent-by-agent Policy Optimization
Authors: Xihuai Wang, Zheng Tian, Ziyu Wan, Ying Wen, Jun Wang, Weinan Zhang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate A2PO, we conduct a comprehensive empirical study on four benchmarks: Star Craft II, Multiagent Mu Jo Co, Multi-agent Particle Environment, and Google Research Football full game scenarios. A2PO consistently outperforms strong baselines. |
| Researcher Affiliation | Collaboration | Xihuai Wang1,2 , Zheng Tian3 , Ziyu Wan1,2, Ying Wen1, Jun Wang2,4, Weinan Zhang1 1 Shanghai Jiao Tong University, 2 Digital Brain Lab, 3 Shanghai Tech University, 4 University College London |
| Pseudocode | Yes | Algorithm 1: Agent-by-agent Policy Optimization (A2PO) and Algorithm 2: Agent-by-agent Policy Optimization (Parameter Sharing) |
| Open Source Code | Yes | The source code of this paper is available at https:// anonymous.4open.science/r/A2PO. |
| Open Datasets | Yes | Star Craft II Multi-agent Challenge (SMAC) (Samvelyan et al., 2019), Multi-agent Mu Jo Co (MA-Mu Jo Co) (de Witt et al., 2020), Multi-agent Particle Environment (MPE) (Lowe et al., 2017)3, and more challenging Google Research Football full-game scenarios (Kurach et al., 2020). |
| Dataset Splits | No | The paper evaluates reinforcement learning agents in simulated environments (Star Craft II, Mu Jo Co, MPE, GRF). These environments involve continuous interaction and episodic learning, not static datasets with pre-defined train/validation/test splits. Performance metrics are gathered from agent-environment interactions rather than partitioned datasets. |
| Hardware Specification | No | No explicit hardware specifications (e.g., specific GPU/CPU models, memory details) are mentioned for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, PyTorch version, CUDA version). |
| Experiment Setup | Yes | We tune several hyper-parameters in all the benchmarks, other hyper-parameters refer to the settings used in MAPPO. cϵ are selected to be 0.5 in all the tasks. (B.4 Hyper-parameters) And subsequent tables like Table 7, 8, 9, 10, 11 detail specific hyperparameter values for each task (e.g., ppo epoch, actor lr, critic lr, λ, ϵ). |