Hierarchical Transformers are Efficient Meta-Reinforcement Learners
<|reference_start|>Hierarchical Transformers are Efficient Meta-Reinforcement Learners: 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.<|reference_end|>
@article{shala2024hierarchical,
    title={Hierarchical Transformers are Efficient Meta-Reinforcement Learners},
    author={Gresa Shala, Andr'e Biedenkapp, Josif Grabocka},
    journal={arXiv preprint arXiv:2402.06402},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06402},
    primaryClass={cs.LG cs.AI}
}