Hierarchical Transformers are Efficient Meta-Reinforcement Learners
Gresa Shala André Biedenkapp Josif Grabocka
We introduce Hierarchical Transformers for Meta-Reinforcement Learning
(HTrMRL), a powerful online meta-reinforcement learning approach. HTrMRL aims
to address the challenge of enabling reinforcement learning agents to perform
effectively in previously unseen tasks. We demonstrate how past episodes serve
as a rich source of information, which our model effectively distills and
applies to new contexts. Our learned algorithm is capable of outperforming the
previous state-of-the-art and provides more efficient meta-training while
significantly improving generalization capabilities. Experimental results,
obtained across various simulated tasks of the Meta-World Benchmark, indicate a
significant improvement in learning efficiency and adaptability compared to the
state-of-the-art on a variety of tasks. Our approach not only enhances the
agent's ability to generalize from limited data but also paves the way for more
robust and versatile AI systems.