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Towards Unsupervised Multi-Agent Reinforcement Learning via Task-Agnostic Exploration

Created by
  • Haebom

Author

Riccardo Zamboni, Mirco Mutti, Marcello Restelli

Outline

This paper focuses on unsupervised dictionary learning in multi-agent environments, especially task-agnostic exploration. While task-agnostic exploration has been well studied in single-agent environments through entropy maximization, it remains an unexplored area in multi-agent environments. This paper characterizes various problem formulations and emphasizes that the problem is difficult in practice despite its theoretical solvability. Then, we present a scalable and distributed trust region policy search algorithm to solve the problem in real environments, and show through numerical verification that it achieves a balance between ease of inference and performance through mixture entropy optimization.

Takeaways, Limitations

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Takeaways:
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A novel approach to unsupervised pre-learning problems in multi-agent environments
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Development of a scalable and distributed trust domain policy exploration algorithm
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Experimentally demonstrated that mixed entropy optimization provides a good trade-off between ease of inference and performance.
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Laying the foundation for unsupervised pre-learning in diverse multi-agent environments
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Limitations:
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Further research is needed to determine whether the performance of the proposed algorithm is optimal in all multi-agent environments.
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Further exploration of other objective functions besides mixed entropy is needed.
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Need to evaluate algorithm efficiency in environments with high-dimensional state spaces or complex interactions
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