# QPLEX: Duplex Dueling Multi-Agent Q-Learning

arXiv: https://arxiv.org/abs/2008.01062 · HTML: https://mlsanity.com/paper/2008.01062/

## Key facts

- Cited by 270 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 30 of 270)

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- [LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions](https://mlsanity.com/paper/2405.11106/) (2024)
- [An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.03052/) (2024)
- [Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration](https://mlsanity.com/paper/2111.11032/) (2021)
- [Towards a Standardised Performance Evaluation Protocol for Cooperative MARL](https://mlsanity.com/paper/2209.10485/) (2022)
- [Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization](https://mlsanity.com/paper/2307.11620/) (2023)
- [Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2305.01738/) (2023)
- [Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2309.12696/) (2023)
- [On Transforming Reinforcement Learning by Transformer: The Development Trajectory](https://mlsanity.com/paper/2212.14164/) (2022)
- [Offline Decentralized Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.01832/) (2021)
- [Learning Long-Term Reward Redistribution via Randomized Return Decomposition](https://mlsanity.com/paper/2111.13485/) (2021)
- [Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.07505/) (2022)
- [RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents](https://mlsanity.com/paper/2102.08159/) (2021)
- [A Review of Cooperation in Multi-agent Learning](https://mlsanity.com/paper/2312.05162/) (2023)
- [Context-Aware Sparse Deep Coordination Graphs](https://mlsanity.com/paper/2106.02886/) (2021)
- [SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning](https://mlsanity.com/paper/2105.15013/) (2021)
- [Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.00136/) (2021)
- [Order Matters: Agent-by-agent Policy Optimization](https://mlsanity.com/paper/2302.06205/) (2023)
- [Embodied AI: From LLMs to World Models](https://mlsanity.com/paper/2509.20021/) (2025)
- [POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding](https://mlsanity.com/paper/2407.14931/) (2024)
- [TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play](https://mlsanity.com/paper/2302.07515/) (2023)
- [MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization](https://mlsanity.com/paper/2302.10418/) (2023)
- [RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization](https://mlsanity.com/paper/2311.01753/) (2023)
- [Regularized Softmax Deep Multi-Agent Q-Learning](https://mlsanity.com/paper/2103.11883/) (2021)
- [HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism](https://mlsanity.com/paper/2110.07246/) (2021)
- [More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization](https://mlsanity.com/paper/2209.12681/) (2022)

## Abstract (arXiv)

We explore value-based multi-agent reinforcement learning (MARL) in the popular paradigm of centralized training with decentralized execution (CTDE). CTDE has an important concept, Individual-Global-Max (IGM) principle, which requires the consistency between joint and local action selections to support efficient local decision-making. However, in order to achieve scalability, existing MARL methods either limit representation expressiveness of their value function classes or relax the IGM consistency, which may suffer from instability risk or may not perform well in complex domains. This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function. This duplex dueling structure encodes the IGM principle into the neural network architecture and thus enables efficient value function learning. Theoretical analysis shows that QPLEX achieves a complete IGM function class. Empirical experiments on StarCraft II micromanagement tasks demonstrate that QPLEX significantly outperforms state-of-the-art baselines in both online and offline data collection settings, and also reveal that QPLEX achieves high sample efficiency and can benefit from offline datasets without additional online exploration.