A model-based approach to meta-Reinforcement Learning: Transformers and tree search

Understand

Meta-learning is a line of research that develops the ability to leverage past experiences to efficiently solve new learning problems.

• Meta-Reinforcement Learning (meta-RL) methods demonstrate a capability to learn behaviors that efficiently acquire and exploit information in several meta-RL problems.
• In this context, the Alchemy benchmark has been proposed by Wang et al.
• [2021].