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

arXiv:2403.09859v1 Announce Type: new
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. …

abstract algorithms arxiv cs.lg domains efficiency exploration focus framework low mamba meta observable reinforcement reinforcement learning sample type world world model