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

• URL: http://arxiv.org/abs/2403.09859v1
• Date: Thu, 14 Mar 2024 20:40:36 GMT
• Title: MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
• Authors: Zohar Rimon, Tom Jurgenson, Orr Krupnik, Gilad Adler, Aviv Tamar,
• Abstract summary: We 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. In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents.
• Score: 18.82398325614491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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