Learning to ground multi-agent reinforcement learning with masked multi-agent AutoEncoding
Mingxiao Feng1, Lin Liu1, Wengang Zhou1
1CAS Key Laboratory of GIPAS, University of Science and Technology of China, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
Summary
Masked Multi-Agent Autoencoding (MMAAE) enhances multi-agent reinforcement learning (MARL) data efficiency by learning robust agent representations. This self-supervised method improves sample efficiency without extra environment interactions.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Robotics
Background:
- Multi-agent reinforcement learning (MARL) faces data efficiency challenges, particularly with partial observability.
- Current methods struggle to leverage shared structures among agents, hindering convergence.
- Limited local observations restrict individual agent performance.
Purpose of the Study:
- Introduce Masked Multi-Agent Autoencoding (MMAAE), a self-supervised framework for MARL.
- Enhance data efficiency and convergence speed in MARL under partial observability.
- Improve the learning of inter-agent dependencies and global state information.
Main Methods:
- Propose MMAAE, a self-supervised representation learning framework for CTDE MARL.
- Implement a dual-space masking mechanism for simultaneous masking and reconstruction in observation and representation spaces.
- Utilize an asymmetric autoencoder and contrastive objective to capture agent dependencies.
Main Results:
- MMAAE significantly boosts sample efficiency in MARL, reducing Time-to-Threshold by up to 42%.
- Achieved up to 38% increase in early-training Area Under the Curve (AUC).
- Maintained or surpassed final performance compared to strong baselines across SMAC, Multi-Agent MuJoCo, and MAQC benchmarks.
Conclusions:
- MMAAE effectively addresses data efficiency bottlenecks in MARL.
- The dual-masking strategy is critical for capturing inter-agent dependencies and global state.
- MMAAE offers a promising approach for improving MARL performance with limited data.
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