# LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2405.19998 · HTML: https://mlsanity.com/paper/2405.19998/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2024.

## Built on by (most cited first, 5 of 5)

- [Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration](https://mlsanity.com/paper/2509.10656/) (2025)
- [A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.17697/) (2025)
- [Social World Model-Augmented Mechanism Design Policy Learning](https://mlsanity.com/paper/2510.19270/) (2025)
- [Trajectory-Class-Aware Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.01440/) (2025)
- [LOD: Latent Objective Discovery in Heterogeneous Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2505.09756/) (2025)

## Abstract (arXiv)

In cooperative multi-agent reinforcement learning (MARL), agents collaborate to achieve common goals, such as defeating enemies and scoring a goal. However, learning goal-reaching paths toward such a semantic goal takes a considerable amount of time in complex tasks and the trained model often fails to find such paths. To address this, we present LAtent Goal-guided Multi-Agent reinforcement learning (LAGMA), which generates a goal-reaching trajectory in latent space and provides a latent goal-guided incentive to transitions toward this reference trajectory. LAGMA consists of three major components: (a) quantized latent space constructed via a modified VQ-VAE for efficient sample utilization, (b) goal-reaching trajectory generation via extended VQ codebook, and (c) latent goal-guided intrinsic reward generation to encourage transitions towards the sampled goal-reaching path. The proposed method is evaluated by StarCraft II with both dense and sparse reward settings and Google Research Football. Empirical results show further performance improvement over state-of-the-art baselines.