Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings

2026-02-16 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

MMSA, a novel model-based multi-agent reinforcement learning (MARL) framework, unifies joint state-action representation learning with imaginative roll-outs to enhance coordination and data efficiency in complex, partially observable environments. It integrates a world model trained with variational auto-encoders (VAEs) and augments it with State-Action Learned Embeddings (SALE). SALE is crucial for both the imagination module, which forecasts future trajectories, and the joint agent network, where individual action values are combined via a mixing network to estimate the joint action-value function. This coupling allows agents to better understand how their actions influence collective outcomes, improving long-term planning with limited real-environment interactions. Empirical studies on benchmarks like StarCraft II Micro-Management, Multi-Agent MuJoCo, and Level-Based Foraging demonstrate MMSA's consistent performance gains over baseline algorithms.

Key takeaway

For research scientists developing advanced MARL systems, MMSA offers a robust framework to overcome sample inefficiency and coordination challenges. You should consider integrating joint state-action learned embeddings and imagination-based planning into your model-based MARL designs, particularly for environments with partial observability and high dynamics. This approach can lead to superior performance and generalizability compared to traditional model-free or less integrated model-based methods.

Key insights

MMSA combines model-based MARL with joint state-action representation learning for enhanced coordination and sample efficiency.

Principles

• Unify representation learning with imaginative roll-outs.
• Decouple SALE encoder training from policy/value functions.
• Use KL balancing to prevent posterior collapse.

Method

MMSA trains a VAE-augmented world model with SALE for latent space roll-outs, integrates SALE into a joint agent network, and uses a QMIX-style mixing network under the CTDE paradigm, optimizing a combined loss function.

In practice

• Apply AvgL1Norm for stable state representation normalization.
• Set roll-out horizon to 3 steps for optimal performance.
• Include global state in mixing network for effective coordination.

Topics

• Multi-Agent Reinforcement Learning
• Model-Based Reinforcement Learning
• Joint State-Action Embeddings
• Variational Auto-Encoders
• Value Decomposition Networks

Best for: Research Scientist, AI Researcher, AI Scientist, Deep Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.