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METTA: Memory-Enhanced Test-Time Adaptation for Meta-Reinforcement Learning

Ali Najar, Hosein Hasani, Amir Kooshan Fattah, Yasamin Rajabi, Mahdieh Soleymani Baghshah · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31656 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

Meta-Reinforcement Learning旨在培养从有限的交互中快速适应未见任务的代理人,但这需要代表长时间交互历史的任务证据。经典的重复性代理人将经验压缩到隐藏的状态中,而基于转换器的代理人则从过去的轨道中获取在方程式记忆和计算成本上快速适应的代理人。在这项工作中,我们提出了 Memory-Enhanced Test-Time Adaptation (METTA) 支持长距离跨事件任务,而不参加完整的交互历史。此外,它将快速适应行为阶段分解为两个时间尺度:因果神经记忆可以进行精细细细细细细细的内部序列调整,并在线性序列复杂性下进行调整,而紧凑的序列总结支持长时间跨事件任务

Machine translation (Meta M2M100); the English original follows.

Abstract

Meta-reinforcement learning aims to train agents that adapt quickly to unseen tasks from limited interaction, but this requires representing task evidence over long interaction histories. Classical recurrent agents compress experience into hidden states, while transformer-based agents retrieve from past trajectories at quadratic memory and computation cost. In this work, we propose Memory-Enhanced Test-Time Adaptation (METTA) for meta-reinforcement learning. It decomposes the fast adaptation phase into two temporal scales: a causal neural memory performs fine-grained within-episode adaptation with linear sequence complexity, while compact episode-level summaries support long-range cross-episode task inference without attending over the full interaction history. Moreover, equipping agents with neural memory provides sufficient expressive capacity and test-time training capability, enabling effective adaptation to unseen held-out tasks and task families at meta-test time. On unseen held-out Meta-World ML10/45 test environments, METTA achieves a success rate over $50\%$ while maintaining a low training budget and short wall-clock time. On long-context sparse-reward PointMaze tasks, it outperforms transformer-based baselines by exhibiting more structured exploration behavior. These results show that METTA provides an efficient path toward more adaptive and scalable long-context meta-RL.

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Topics: Computer science, Task (project management), Adaptation (eye), Artificial intelligence, Scalability, Machine learning

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