MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning

Zohar Rimon; Tom Jurgenson; Orr Krupnik; Gilad Adler; Aviv Tamar

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arXiv · 2403.09859

MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning

Zohar Rimon·Tom Jurgenson·Orr Krupnik·Gilad Adler·Aviv Tamar

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Abstract

Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distributions. In parallel, model-based RL methods have been successful in solving partially observable MDPs, of which meta-RL is a special case. In this work, we leverage this success and propose a new model-based approach to meta-RL, based on elements from existing state-of-the-art model-based and meta-RL methods. We demonstrate the effectiveness of our approach on common meta-RL benchmark domains, attaining greater return with better sample efficiency (up to $15\times$) while requiring very little hyperparameter tuning. In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents.

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Zohar Rimon, Tom Jurgenson, Orr Krupnik, Gilad Adler, Aviv Tamar. 2024-03-14. MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning. https://arxiv.org/abs/2403.09859

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