mlsanityData to 7 Oct 2026

Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning

arXiv 2406.02890 ↗First posted 5 Jun 2024cs.MA cs.AI cs.LGcited by 3

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Abstract (arXiv)

Sample efficiency remains a key challenge in multi-agent reinforcement learning (MARL). A promising approach is to learn a meaningful latent representation space through auxiliary learning objectives alongside the MARL objective to aid in learning a successful control policy. In our work, we present MAPO-LSO (Multi-Agent Policy Optimization with Latent Space Optimization) which applies a form of comprehensive representation learning devised to supplement MARL training. Specifically, MAPO-LSO proposes a multi-agent extension of transition dynamics reconstruction and self-predictive learning that constructs a latent state optimization scheme that can be trivially extended to current state-of-the-art MARL algorithms. Empirical results demonstrate MAPO-LSO to show notable improvements in sample efficiency and learning performance compared to its vanilla MARL counterpart without any additional MARL hyperparameter tuning on a diverse suite of MARL tasks.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 44 of its 60 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

44 of 60 references.

References · 44

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32 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Multi-agent Auto-Bidding with Latent Graph Diffusion Modelscited by 1
  2. 2025Unifying Agent Interaction and World Information for Multi-agent Coordinationcited by 1
  3. 2026MA-VLCM: A Vision Language Critic Model for Value Estimation of Policies in Multi-Agent Team Settingscited by 0