By splitting task adaptation into fast within-episode memory updates and slower cross-episode summaries, METTA enables meta-RL agents to handle long interaction histories efficiently without the quadratic cost of transformer attention.
METTA proposes a memory-enhanced approach for meta-reinforcement learning that adapts quickly to new tasks by combining a causal neural memory for fine-grained within-episode learning with compact episode summaries for long-range task inference.