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..
FACMAC: Factored Multi-Agent Centralised Policy Gradients
Authors: Bei Peng, Tabish Rashid, Christian Schroeder de Witt, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Boehmer, Shimon Whiteson
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate FACMAC on variants of the multi-agent particle environments, a novel multi-agent Mu Jo Co benchmark, and a challenging set of Star Craft II micromanagement tasks. Empirical results demonstrate FACMAC s superior performance over MADDPG and other baselines on all three domains. |
| Researcher Affiliation | Collaboration | Bei Peng University of Liverpool Tabish Rashid University of Oxford Christian A. Schroeder de Witt University of Oxford Pierre-Alexandre Kamienny Facebook AI Research Philip H. S. Torr University of Oxford Wendelin Böhmer Delft University of Technology Shimon Whiteson University of Oxford |
| Pseudocode | No | The paper describes algorithms and derivations but does not present a formal pseudocode block or an algorithm box. |
| Open Source Code | Yes | Code is available at https://github.com/oxwhirl/facmac. |
| Open Datasets | Yes | We evaluate FACMAC on variants of the multi-agent particle environments [23]... and the challenging SMAC benchmark [35]... |
| Dataset Splits | No | The paper describes using a replay buffer and mini-batches, and mentions training for a certain number of timesteps. It also uses 'test' win rate, but does not explicitly describe a train/validation/test split for the datasets themselves (e.g., in terms of percentages or counts). |
| Hardware Specification | Yes | All experiments were run on a single NVIDIA GeForce RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Open AI Gym [5]' and 'PyMARL [35] framework' and 'MuJoCo', but it does not specify version numbers for any software dependencies. It mentions 'SC2.4.10.' but that's a version of StarCraft II, not a software dependency for their code. |
| Experiment Setup | Yes | We use Adam optimizer [17]... learning rate 5e-4... replay buffer of size 1e6... batch size 32... hidden layer size 64 for all networks... training for 2 million timesteps... The discount factor γ is 0.99... Target networks are updated using polyak averaging with smoothing coefficient τ = 0.005. |