Research map / Multi-agent systems and games / Cooperative multi-agent RL
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning (ICML 2021 · similarity 0.92)
- QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (ICML 2019 · similarity 0.91)
- Multiagent Q-learning with Sub-Team Coordination (NeurIPS 2022 · similarity 0.91)
- ConcaveQ: Non-monotonic Value Function Factorization via Concave Representations in Deep Multi-Agent Reinforcement Learning (AAAI 2024 · similarity 0.90)
- High-order Interactions Modeling for Interpretable Multi-Agent Q-Learning (NeurIPS 2025 · similarity 0.89)
- Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning (NeurIPS 2024 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.