# Offline Decentralized Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.01832 · HTML: https://mlsanity.com/paper/2108.01832/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 48 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 30 of 30)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction](https://mlsanity.com/paper/1906.00949/) (2019)
- [Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1910.00177/) (2019)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Behavior Regularized Offline Reinforcement Learning](https://mlsanity.com/paper/1911.11361/) (2019)
- [MOPO: Model-based Offline Policy Optimization](https://mlsanity.com/paper/2005.13239/) (2020)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [COMBO: Conservative Offline Model-Based Policy Optimization](https://mlsanity.com/paper/2102.08363/) (2021)
- [Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog](https://mlsanity.com/paper/1907.00456/) (2019)
- [QPLEX: Duplex Dueling Multi-Agent Q-Learning](https://mlsanity.com/paper/2008.01062/) (2020)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning](https://mlsanity.com/paper/2105.08140/) (2021)
- [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1702.08887/) (2017)
- [Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.03400/) (2021)
- [Learning Individually Inferred Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/2006.06455/) (2020)
- [Lenient Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1707.04402/) (2017)

## Built on by (most cited first, 30 of 33)

- [MADiff: Offline Multi-agent Learning with Diffusion Models](https://mlsanity.com/paper/2305.17330/) (2023)
- [Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification](https://mlsanity.com/paper/2111.11188/) (2021)
- [Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization](https://mlsanity.com/paper/2307.11620/) (2023)
- [Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2309.12696/) (2023)
- [Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks](https://mlsanity.com/paper/2112.02845/) (2021)
- [Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus](https://mlsanity.com/paper/2206.00159/) (2022)
- [When is Offline Two-Player Zero-Sum Markov Game Solvable?](https://mlsanity.com/paper/2201.03522/) (2022)
- [Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.01888/) (2022)
- [A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem](https://mlsanity.com/paper/2305.17198/) (2023)
- [Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL](https://mlsanity.com/paper/2307.01472/) (2023)
- [Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2211.15612/) (2022)
- [Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks](https://mlsanity.com/paper/2408.10556/) (2024)
- [Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.08421/) (2024)
- [Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation](https://mlsanity.com/paper/2406.09068/) (2024)
- [Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.00521/) (2023)
- [ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization](https://mlsanity.com/paper/2410.01954/) (2024)
- [Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation](https://mlsanity.com/paper/2503.21200/) (2025)
- [OM2P: Offline Multi-Agent Mean-Flow Policy](https://mlsanity.com/paper/2508.06269/) (2025)
- [Value Activation for Bias Alleviation: Generalized-activated Deep Double Deterministic Policy Gradients](https://mlsanity.com/paper/2112.11216/) (2021)
- [MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment](https://mlsanity.com/paper/2312.03644/) (2023)
- [DM2: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching](https://mlsanity.com/paper/2206.00233/) (2022)
- [Coordination Failure in Cooperative Offline MARL](https://mlsanity.com/paper/2407.01343/) (2024)
- [Select to Perfect: Imitating desired behavior from large multi-agent data](https://mlsanity.com/paper/2405.03735/) (2024)
- [Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.12001/) (2024)
- [Offline Multi-agent Reinforcement Learning via Sequential Score Decomposition](https://mlsanity.com/paper/2505.05968/) (2025)
- [Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2302.09605/) (2023)
- [A Recipe for Stable Offline Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2603.08399/) (2026)
- [Beyond Shallow Behavior: Task-Efficient Value-Based Multi-Task Offline MARL via Skill Discovery](https://mlsanity.com/paper/2502.08985/) (2025)
- [Multi-Modal Decentralized Reinforcement Learning for Modular Reconfigurable Lunar Robots](https://mlsanity.com/paper/2510.20347/) (2025)
- [Oryx: a Scalable Sequence Model for Many-Agent Coordination in Offline MARL](https://mlsanity.com/paper/2505.22151/) (2025)

## Abstract (arXiv)

In many real-world multi-agent cooperative tasks, due to high cost and risk, agents cannot continuously interact with the environment and collect experiences during learning, but have to learn from offline datasets. However, the transition dynamics in the dataset of each agent can be much different from the ones induced by the learned policies of other agents in execution, creating large errors in value estimates. Consequently, agents learn uncoordinated low-performing policies. In this paper, we propose a framework for offline decentralized multi-agent reinforcement learning, which exploits value deviation and transition normalization to deliberately modify the transition probabilities. Value deviation optimistically increases the transition probabilities of high-value next states, and transition normalization normalizes the transition probabilities of next states. They together enable agents to learn high-performing and coordinated policies. Theoretically, we prove the convergence of Q-learning under the altered non-stationary transition dynamics. Empirically, we show that the framework can be easily built on many existing offline reinforcement learning algorithms and achieve substantial improvement in a variety of multi-agent tasks.