Hierarchical Transformers are Efficient Meta-Reinforcement Learners
This work addresses the challenge of meta-reinforcement learning for AI systems, offering incremental improvements in efficiency and adaptability for agents in simulated environments.
The paper tackles the problem of enabling reinforcement learning agents to perform effectively in unseen tasks by introducing Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), which outperforms previous state-of-the-art methods and improves learning efficiency and generalization across simulated tasks in the Meta-World Benchmark.
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.