# Group-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2202.05135 · HTML: https://mlsanity.com/paper/2202.05135/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [QPLEX: Duplex Dueling Multi-Agent Q-Learning](https://mlsanity.com/paper/2008.01062/) (2020)
- [Distributed Prioritized Experience Replay](https://mlsanity.com/paper/1803.00933/) (2018)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1702.08887/) (2017)
- [Third-Person Imitation Learning](https://mlsanity.com/paper/1703.01703/) (2017)
- [Massively Parallel Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1507.04296/) (2015)
- [Emergent Social Learning via Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2010.00581/) (2020)
- [SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1911.04094/) (2019)
- [Hybrid Reinforcement Learning with Expert State Sequences](https://mlsanity.com/paper/1903.04110/) (2019)
- [Distributed Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/2011.11012/) (2020)
- [Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.06042/) (2021)
- [SaLinA: Sequential Learning of Agents](https://mlsanity.com/paper/2110.07910/) (2021)
- [IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks](https://mlsanity.com/paper/1912.00167/) (2019)

## Built on by (most cited first, 1 of 1)

- [Group-Agent Reinforcement Learning with Heterogeneous Agents](https://mlsanity.com/paper/2501.11818/) (2025)

## Abstract (arXiv)

It can largely benefit the reinforcement learning (RL) process of each agent if multiple geographically distributed agents perform their separate RL tasks cooperatively. Different from multi-agent reinforcement learning (MARL) where multiple agents are in a common environment and should learn to cooperate or compete with each other, in this case each agent has its separate environment and only communicates with others to share knowledge without any cooperative or competitive behaviour as a learning outcome. In fact, this scenario exists widely in real life whose concept can be utilised in many applications, but is not well understood yet and not well formulated. As the first effort, we propose group-agent system for RL as a formulation of this scenario and the third type of RL system with respect to single-agent and multi-agent systems. We then propose a distributed RL framework called DDAL (Decentralised Distributed Asynchronous Learning) designed for group-agent reinforcement learning (GARL). We show through experiments that DDAL achieved desirable performance with very stable training and has good scalability.